{ "paper_id": "2021", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T10:43:50.873019Z" }, "title": "Massive Choice, Ample Tasks (MACHAMP): A Toolkit for Multi-task Learning in NLP", "authors": [ { "first": "Rob", "middle": [], "last": "Van Der", "suffix": "", "affiliation": {}, "email": "robv@itu.dk" }, { "first": "Goot", "middle": [], "last": "Ahmet\u00fcst\u00fcn", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Alan", "middle": [], "last": "Ramponi", "suffix": "", "affiliation": {}, "email": "alan.ramponi@unitn.it" }, { "first": "Ibrahim", "middle": [], "last": "Sharaf", "suffix": "", "affiliation": {}, "email": "ibrahim.sharaf@factmata.com" }, { "first": "Barbara", "middle": [], "last": "Plank", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Amirhossein", "middle": [ "Mojiri" ], "last": "Foroushani", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Hamid", "middle": [], "last": "Aghaei", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Ahmadi", "middle": [ "2020a" ], "last": "Ud", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Ahmadi", "middle": [ "2020b" ], "last": "Ud", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Kadri", "middle": [], "last": "Muischnek", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Kaili", "middle": [], "last": "M\u00fc\u00fcrisep", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Tiina", "middle": [], "last": "Puolakainen", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Eleri", "middle": [], "last": "Aedmaa", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Riin", "middle": [], "last": "Kirt", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Dage", "middle": [ "2014" ], "last": "S\u00e4rg", "suffix": "", "affiliation": {}, "email": "" }, { "first": "", "middle": [], "last": "Esto", "suffix": "", "affiliation": {}, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "Transfer learning, particularly approaches that combine multi-task learning with pre-trained contextualized embeddings and fine-tuning, have advanced the field of Natural Language Processing tremendously in recent years. In this paper we present MACHAMP, a toolkit for easy fine-tuning of contextualized embeddings in multi-task settings. The benefits of MACHAMP are its flexible configuration options, and the support of a variety of natural language processing tasks in a uniform toolkit, from text classification and sequence labeling to dependency parsing, masked language modeling, and text generation.", "pdf_parse": { "paper_id": "2021", "_pdf_hash": "", "abstract": [ { "text": "Transfer learning, particularly approaches that combine multi-task learning with pre-trained contextualized embeddings and fine-tuning, have advanced the field of Natural Language Processing tremendously in recent years. In this paper we present MACHAMP, a toolkit for easy fine-tuning of contextualized embeddings in multi-task settings. The benefits of MACHAMP are its flexible configuration options, and the support of a variety of natural language processing tasks in a uniform toolkit, from text classification and sequence labeling to dependency parsing, masked language modeling, and text generation.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "Multi-task learning (MTL) (Caruana, 1993 (Caruana, , 1997 has developed into a standard repertoire in natural language processing (NLP). It enables neural networks to learn tasks in parallel (Caruana, 1993) while leveraging the benefits of sharing parameters. The shift-or \"tsunami\" (Manning, 2015)-of deep learning in NLP has facilitated the wide-spread use of MTL since the seminal work by Collobert et al. (2011) , which has led to a multi-task learning \"wave\" (Ruder and Plank, 2018) in NLP. It has since been applied to a wide range of NLP tasks, developing into a viable alternative to classical pipeline approaches. This includes early adoption in Recurrent Neural Network models, e.g. (Lazaridou et al., 2015; Chrupa\u0142a et al., 2015; Plank et al., 2016; S\u00f8gaard and Goldberg, 2016; Hashimoto et al., 2017) , to the use of large pre-trained language models with multi-task objectives (Radford et al., 2019; Devlin et al., 2019) . MTL comes in many flavors, based on the type of sharing, the weighting of losses, and the design and relations of tasks and layers. In general though, outperforming single-task settings remains a challenge (Mart\u00ednez Alonso and Plank, 2017; Clark et al., 2019) . For an overview of MTL in NLP we refer to Ruder (2017) .", "cite_spans": [ { "start": 26, "end": 40, "text": "(Caruana, 1993", "ref_id": null }, { "start": 41, "end": 57, "text": "(Caruana, , 1997", "ref_id": null }, { "start": 191, "end": 206, "text": "(Caruana, 1993)", "ref_id": null }, { "start": 392, "end": 415, "text": "Collobert et al. (2011)", "ref_id": "BIBREF11" }, { "start": 464, "end": 487, "text": "(Ruder and Plank, 2018)", "ref_id": "BIBREF84" }, { "start": 693, "end": 717, "text": "(Lazaridou et al., 2015;", "ref_id": null }, { "start": 718, "end": 740, "text": "Chrupa\u0142a et al., 2015;", "ref_id": "BIBREF5" }, { "start": 741, "end": 760, "text": "Plank et al., 2016;", "ref_id": null }, { "start": 761, "end": 788, "text": "S\u00f8gaard and Goldberg, 2016;", "ref_id": "BIBREF106" }, { "start": 789, "end": 812, "text": "Hashimoto et al., 2017)", "ref_id": null }, { "start": 890, "end": 912, "text": "(Radford et al., 2019;", "ref_id": "BIBREF76" }, { "start": 913, "end": 933, "text": "Devlin et al., 2019)", "ref_id": "BIBREF16" }, { "start": 1142, "end": 1175, "text": "(Mart\u00ednez Alonso and Plank, 2017;", "ref_id": "BIBREF54" }, { "start": 1176, "end": 1195, "text": "Clark et al., 2019)", "ref_id": "BIBREF10" }, { "start": 1240, "end": 1252, "text": "Ruder (2017)", "ref_id": "BIBREF83" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "As a separate line of research, the idea of language model pre-training and contextual embeddings (Howard and Ruder, 2018; Devlin et al., 2019) is to pre-train rich representation on large quantities of monolingual or multilingual text data. Taking these representations as a starting point has led to enormous improvements across a wide variety of NLP problems. Related to MTL, recent research effort focuses on fine-tuning contextualized embeddings on a variety of tasks with supervised objectives (Kondratyuk and Straka, 2019; Sanh et al., 2019; Hu et al., 2020) .", "cite_spans": [ { "start": 98, "end": 122, "text": "(Howard and Ruder, 2018;", "ref_id": null }, { "start": 123, "end": 143, "text": "Devlin et al., 2019)", "ref_id": "BIBREF16" }, { "start": 516, "end": 529, "text": "Straka, 2019;", "ref_id": null }, { "start": 530, "end": 548, "text": "Sanh et al., 2019;", "ref_id": "BIBREF94" }, { "start": 549, "end": 565, "text": "Hu et al., 2020)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "We introduce MACHAMP, a flexible toolkit for multi-task learning and fine-tuning of NLP problems. The main advantages of MACHAMP are:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "\u2022 Ease of configuration, especially for dealing with multiple datasets and multi-task setups;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "\u2022 Support of a wide range of NLP tasks, including a variety of sequence labeling approaches, text classification, dependency parsing, masked language modeling, and text generation (e.g., machine translation);", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "\u2022 Support of the initialization and fine-tuning of any contextualized embeddings from Hugging Face (Wolf et al., 2020) .", "cite_spans": [ { "start": 99, "end": 118, "text": "(Wolf et al., 2020)", "ref_id": "BIBREF127" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "As a result, the flexibility of MACHAMP supports up-to-date, general-purpose NLP (see Section 2.2). The backbone of MACHAMP is Al-lenNLP , a PyTorch-based (Paszke et al., 2019) Python library containing modules for a variety of deep learning methods and NLP tasks. It is designed to be modular, high- level and flexible. It should be noted that contemporary to MACHAMP, jiant (Pruksachatkun et al., 2020) was developed, and AllenNLP included multi-task learning as well since release 2.0. MACHAMP distinguishes from the other toolkits by supporting simple configurations, and a variety of multi-task settings.", "cite_spans": [ { "start": 155, "end": 176, "text": "(Paszke et al., 2019)", "ref_id": "BIBREF68" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "In this section we will discuss the model, its supported tasks, and possible configuration settings.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Model", "sec_num": "2" }, { "text": "An overview of the model is shown in Figure 1 . MACHAMP takes a pre-trained contextualized model as initial encoder, and fine-tunes its layers by applying an inverse square root learning rate decay with linear warm-up (Howard and Ruder, 2018), according to a given set of downstream tasks. For the task-specific predictions, each task has its own decoder, which is trained for the corresponding task. The model defaults to the embedding-specific tokenizer in Hugging Face (Wolf et al., 2020) . 2 When multiple datasets are used for training, they are first separately split into batches so that each batch only contains instances from one dataset. Batches are then concatenated and shuffled before training. This means that small datasets will be underrepresented, which can be overcome by smoothing the dataset sampling (Section 3.2.2). During de-coding, the loss function is only activated for tasks which are present in the current batch. By default, all tasks have an equal weight in the loss function. The loss weight can be tuned (Section 3.2.1).", "cite_spans": [ { "start": 472, "end": 491, "text": "(Wolf et al., 2020)", "ref_id": "BIBREF127" }, { "start": 494, "end": 495, "text": "2", "ref_id": null } ], "ref_spans": [ { "start": 37, "end": 45, "text": "Figure 1", "ref_id": "FIGREF0" } ], "eq_spans": [], "section": "Model overview", "sec_num": "2.1" }, { "text": "We here describe the tasks MACHAMP supports. SEQ For traditional token-level sequence prediction tasks, like part-of-speech tagging. MACHAMP uses greedy decoding with a softmax output layer on the output of the contextual embeddings.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Supported task types", "sec_num": "2.2" }, { "text": "STRING2STRING An extension to SEQ, which learns a conversion for each input token to its label. Instead of predicting the labels directly, the model can now learn to predict the conversion. This strategy is commonly used for lemmatization (Chrupa\u0142a, 2006; Kondratyuk and Straka, 2019) , where it greatly reduces the label vocabulary. We use the transformation algorithm from UDPipe-Future (Straka, 2018) , which was also used by Kondratyuk and Straka (2019) .", "cite_spans": [ { "start": 239, "end": 255, "text": "(Chrupa\u0142a, 2006;", "ref_id": "BIBREF6" }, { "start": 256, "end": 284, "text": "Kondratyuk and Straka, 2019)", "ref_id": null }, { "start": 389, "end": 403, "text": "(Straka, 2018)", "ref_id": "BIBREF108" }, { "start": 429, "end": 457, "text": "Kondratyuk and Straka (2019)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Supported task types", "sec_num": "2.2" }, { "text": "SEQ BIO A variant of SEQ which exploits conditional random fields (Lafferty et al., 2001 ) as decoder, masked to enforce outputs following the BIO tagging scheme.", "cite_spans": [ { "start": 66, "end": 88, "text": "(Lafferty et al., 2001", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Supported task types", "sec_num": "2.2" }, { "text": "MULTISEQ An extension to SEQ which supports the prediction of multiple labels per token. Specifically, for some sequence labeling tasks it is unknown beforehand how many labels each token should get. We compute a probability score for each label, employing binary cross-entropy as loss, and outputting all the labels that exceed a certain threshold. The threshold can be set in the dataset configuration file.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Supported task types", "sec_num": "2.2" }, { "text": "DEPENDENCY For dependency parsing, MACHAMP uses the deep biaffine parser (Dozat and Manning, 2017) as implemented by Al-lenNLP , with the Chu-Liu/Edmonds algorithm (Chu, 1965; Edmonds, 1967) for decoding the tree.", "cite_spans": [ { "start": 73, "end": 98, "text": "(Dozat and Manning, 2017)", "ref_id": "BIBREF22" }, { "start": 164, "end": 175, "text": "(Chu, 1965;", "ref_id": "BIBREF7" }, { "start": 176, "end": 190, "text": "Edmonds, 1967)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Supported task types", "sec_num": "2.2" }, { "text": "MLM For masked language modeling, our implementation follows the original BERT settings (Devlin et al., 2019) . The chance that a token is masked is 15%, of which 80% are masked with a [MASK] token, 10% with a random token, and 10% are left unchanged. We do not include the next sentence prediction task following Liu et al. (2019) , for simplicity and efficiency. We use a cross entropy loss, smell VERB ya PRON later ADV ! PUNCT (a) Example of a token-level file format (e.g., for POS tagging), where words are in column word idx=0, and a single layer of corresponding annotations is in column column idx=1.", "cite_spans": [ { "start": 88, "end": 109, "text": "(Devlin et al., 2019)", "ref_id": "BIBREF16" }, { "start": 185, "end": 191, "text": "[MASK]", "ref_id": null }, { "start": 314, "end": 331, "text": "Liu et al. (2019)", "ref_id": "BIBREF45" } ], "ref_spans": [], "eq_spans": [], "section": "Supported task types", "sec_num": "2.2" }, { "text": "smell ya later ! negative (b) Example of a sentence-level file format (e.g., for sentiment classification), where only a sentence is required and is defined in column 0 (i.e., sent idxs=[0]) and a single layer of annotation is in the second column (column idx=1). and the language model heads from the defined Hugging Face embeddings (Wolf et al., 2020) . It assumes raw text files as input, so no column idx has to be defined (See Section 3.1).", "cite_spans": [ { "start": 334, "end": 353, "text": "(Wolf et al., 2020)", "ref_id": "BIBREF127" } ], "ref_spans": [], "eq_spans": [], "section": "Supported task types", "sec_num": "2.2" }, { "text": "CLASSIFICATION For text classification, it predicts a label for every text instance by using the embedding of the first token, which is commonly a special token (e.g.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Supported task types", "sec_num": "2.2" }, { "text": "[CLS] or ). For tasks which model a relation between multiple sentences (e.g., textual entailment), a special token (e.g. [SEP] ) is automatically inserted between the sentences to inform the model about the sentence boundaries.", "cite_spans": [ { "start": 125, "end": 130, "text": "[SEP]", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Supported task types", "sec_num": "2.2" }, { "text": "SEQ2SEQ For text generation, MACHAMP employs the sequence to sequence (encoder-decoder) paradigm (Sutskever et al., 2014) . We use a recurrent neural network decoder, which suits the auto-regressive nature of the machine translation tasks (Cho et al., 2014) and an attention mechanism to avoid compressing the whole source sentence into a fixed-length vector (Bahdanau et al., 2015) .", "cite_spans": [ { "start": 97, "end": 121, "text": "(Sutskever et al., 2014)", "ref_id": "BIBREF111" }, { "start": 239, "end": 257, "text": "(Cho et al., 2014)", "ref_id": "BIBREF4" }, { "start": 359, "end": 382, "text": "(Bahdanau et al., 2015)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Supported task types", "sec_num": "2.2" }, { "text": "To use MACHAMP, one needs a configuration file, input data and a command to start the training or prediction. In this section we will describe each of these requirements.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Usage", "sec_num": "3" }, { "text": "MACHAMP supports two types of data formats for annotated data, 3 which correspond to the level of annotation (Section 2.2). For token-level tasks, we will use the term \"token-level file format\", whereas for sentence-level task, we will use \"sentence-level file format\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Data format", "sec_num": "3.1" }, { "text": "The token-level file format is similar to the tabseparated CoNLL format (Tjong Kim Sang and De Meulder, 2003) . It assumes one token per line (on a column index word idx), with each annotation layer following each token separated by a tab character (each on a column index column idx) (Figure 2a ). Token sequences (e.g., sentences) are delimited by an empty line. Comments are lines on top of the sequence (which have a different number of columns with respect to \"token lines\"). 4 It should be noted that for dependency parsing, the format assumes the relation label to be on the column idx and the head index on the following column. Further, we also support the UD format by removing multi-word tokens and empty nodes using the UD-conversion-tools (Agi\u0107 et al., 2016) .", "cite_spans": [ { "start": 72, "end": 109, "text": "(Tjong Kim Sang and De Meulder, 2003)", "ref_id": "BIBREF113" }, { "start": 481, "end": 482, "text": "4", "ref_id": null }, { "start": 752, "end": 771, "text": "(Agi\u0107 et al., 2016)", "ref_id": null } ], "ref_spans": [ { "start": 285, "end": 295, "text": "(Figure 2a", "ref_id": "FIGREF1" } ], "eq_spans": [], "section": "Data format", "sec_num": "3.1" }, { "text": "The sentence-level file format (used for text classification and text generation) is similar ( Figure 2b ), and also supports multiple inputs having the same annotation layers. A list of one or more column indices can be defined (i.e., sent idxs) to enable modeling the relation between any arbitrary number of sentences.", "cite_spans": [], "ref_spans": [ { "start": 95, "end": 104, "text": "Figure 2b", "ref_id": "FIGREF1" } ], "eq_spans": [], "section": "Data format", "sec_num": "3.1" }, { "text": "The model requires two configuration files, one that specifies the datasets and tasks, and one for the hyperparameters. For the hyperparameters, a default option is provided (configs/params.json, see Section 4).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Configuration", "sec_num": "3.2" }, { "text": "An example of a dataset configuration file is shown in Figure 3 . On the first level, the dataset names are specified (i.e., \"UD\" and \"RTE\"), which should be unique identifiers. Each of these datasets needs at least a train data path, a validation data path, a word idx or sent idxs, and a list of tasks (corresponding to the layers of annotation, see Section 3.1).", "cite_spans": [], "ref_spans": [ { "start": 55, "end": 63, "text": "Figure 3", "ref_id": null } ], "eq_spans": [], "section": "Dataset configuration", "sec_num": "3.2.1" }, { "text": "For each of the defined tasks, the user is required to define the task type (Section 2.2), and the column index from which to read the relevant labels (i.e., column idx). On top of this template, the following options can be passed on the task level:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Dataset configuration", "sec_num": "3.2.1" }, { "text": "{ \"UD\": { \"train_data_path\": \"data/ewt.train\", \"validation_data_path\": \"data/ewt.dev\", \"word_idx\": 1, \"tasks\": { \"lemma\": { \"task_type\": \"string2string\", \"column_idx\": 2 }, \"upos\": { \"task_type\": \"seq\", \"column_idx\": 3 } } } \"RTE\": { \"train_data_path\": \"data/RTE.train\", \"validation_data_path\": \"data/RTE.dev\", \"sent_idxs\": [0,1], \"tasks\": { \"rte\": { \"task_type\": \"classification\", \"column_idx\": 2 } } } } Figure 3 : Example dataset configuration file to predict UPOS, lemmas, and textual entailment simultaneously.", "cite_spans": [], "ref_spans": [ { "start": 406, "end": 414, "text": "Figure 3", "ref_id": null } ], "eq_spans": [], "section": "Dataset configuration", "sec_num": "3.2.1" }, { "text": "Metric For each task type, a commonly used metric is set as default metric. However, one can override the default by specifying a different metric at the task level. Supported metrics are 'acc', 'las', 'micro-f1', 'macro-f1', 'span f1', 'multi span f1', 'bleu' and 'perplexity'.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Dataset configuration", "sec_num": "3.2.1" }, { "text": "Loss weight In multi-task settings, not all tasks might be equally important, or some tasks might just be harder to learn, and therefore should gain more weight during training. This can be tuned by setting the loss weight parameter on the task level (by default the value is 1.0 for all tasks). Ammar et al. (2016) have shown that embedding which language an instance belongs to can be beneficial for multilingual models. Later work (Stymne et al., 2018; Wagner et al., 2020) has also shown that more fine-grained distinctions on the dataset level 5 can be beneficial when training on multiple datasets within the same language (family). In previous work, this embedding is usually concatenated to the word embedding before the encoding. However, in contextualized embeddings, the word embeddings themselves are commonly used as encoder, hence we concatenate the dataset embeddings in between the encoder and the decoder. This parameter is set on the dataset level with dataset embed idx, which specifies the column to read the dataset ID from. Setting dataset embed idx to -1 will use the dataset name as specified in the json file as ID.", "cite_spans": [ { "start": 296, "end": 315, "text": "Ammar et al. (2016)", "ref_id": null }, { "start": 434, "end": 455, "text": "(Stymne et al., 2018;", "ref_id": "BIBREF109" }, { "start": 456, "end": 476, "text": "Wagner et al., 2020)", "ref_id": "BIBREF122" } ], "ref_spans": [], "eq_spans": [], "section": "Dataset configuration", "sec_num": "3.2.1" }, { "text": "Max sentences In order to limit the maximum number of sentences that are used during training, max sents is used. This is done before the sampling smoothing (Section 3.2.2), if both are enabled. It should be noted that the specified number will be taken from the top of the dataset.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Dataset embedding", "sec_num": null }, { "text": "Whereas most of the hyperparameters can simply be changed from the default configuration provided in configs/params.json, we would like to highlight two main settings.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Hyperparameter configuration", "sec_num": "3.2.2" }, { "text": "Pre-trained embeddings The name/path to pretrained Hugging Face embeddings 6 can be set in the configuration file at the transformer model key; transformer dim might be adapted accordingly to reflect the embeddings dimension.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Hyperparameter configuration", "sec_num": "3.2.2" }, { "text": "Dataset sampling To avoid larger datasets from overwhelming the model, MACHAMP can resample multiple datasets according to a multinomial distribution, similar as used by Conneau and Lample (2019) . MACHAMP performs the sampling on the batch level, and shuffles after each epoch (so it can see a larger variety of instances for downsampled datasets). The formula is:", "cite_spans": [ { "start": 170, "end": 195, "text": "Conneau and Lample (2019)", "ref_id": "BIBREF13" } ], "ref_spans": [], "eq_spans": [], "section": "Hyperparameter configuration", "sec_num": "3.2.2" }, { "text": "\u03bb = 1 p i * p \u03b1 i i p \u03b1 i (1)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Hyperparameter configuration", "sec_num": "3.2.2" }, { "text": "where p i is the probability that a random sample is from dataset i, and \u03b1 is a hyperparameter that can be set. Setting \u03b1=1.0 means using the default sizes, and \u03b1=0.0 results in one average amount of batches for each dataset, similar to Sanh et al. (2019) . The effect of different settings of \u03b1 for the Universal Dependencies 2.6 data is shown in Figure 4 . Smoothing can be enabled in the hyperparameters configuration file at the sampling smoothing key.", "cite_spans": [ { "start": 237, "end": 255, "text": "Sanh et al. (2019)", "ref_id": "BIBREF94" } ], "ref_spans": [ { "start": 348, "end": 356, "text": "Figure 4", "ref_id": "FIGREF2" } ], "eq_spans": [], "section": "Hyperparameter configuration", "sec_num": "3.2.2" }, { "text": "Given the setup illustrated in the previous sections, a model can be trained via the following command. It assumes the configuration ( Figure 3 ) is saved in configs/upos-lemma-rte.json.", "cite_spans": [], "ref_spans": [ { "start": 135, "end": 143, "text": "Figure 3", "ref_id": null } ], "eq_spans": [], "section": "Training", "sec_num": "3.3" }, { "text": "python3 train.py --dataset_config \\ configs/upos-lemma-rte.json", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training", "sec_num": "3.3" }, { "text": "By default, the model and the logs will be written to logs//. The name of the directory can be set manually by providing --name . Further, --device can be used to specify which GPU to use, otherwise the CPU will be used. As a default, train.py uses configs/params.json for the hyperparameters, but this can be overridden by using --parameters config .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training", "sec_num": "3.3" }, { "text": "Prediction can be done with:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Inference", "sec_num": "3.4" }, { "text": "python3 predict.py \\ logs///model.tar.gz \\ ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Inference", "sec_num": "3.4" }, { "text": "It requires the path to the best model (serialized during training) stored as model.tar.gz in the logs directory as specified above. By default, the data is assumed to be in the same format as the training data (i.e., with the same number of column idx columns), but --raw text can be specified to read a data file containing raw texts with one sentence per line. For models trained on multiple datasets (as \"UD\" and \"RTE\" in Figure 3), --dataset can be used to specify which dataset to use in order to predict all tasks within that dataset.", "cite_spans": [], "ref_spans": [ { "start": 426, "end": 432, "text": "Figure", "ref_id": null } ], "eq_spans": [], "section": "Inference", "sec_num": "3.4" }, { "text": "In this section we describe the procedure how we determined robust default parameters for MACHAMP; note that the goal is not to beat the state-of-the-art, but to reach competitive performance for multiple tasks simultaneously. 7 For the tuning of hyperparameters, we used the GLUE classification datasets (Wang et al., 2018; Warstadt et al., 2019; Socher et al., 2013; Dolan and Brockett, 2005; Cer et al., 2017; Williams et al., 2018; Rajpurkar et al., 2018; Bentivogli et al., 2009; Levesque et al., 2012) and the English Web Treebank (EWT 2.6) (Silveira et al., 2014) with multilingual BERT 8 (mBERT) as embeddings. 9 For each of these setups, we averaged the scores over all datasets/tasks and perform a grid search. The best hyperparameters across all datasets are reported in ", "cite_spans": [ { "start": 227, "end": 228, "text": "7", "ref_id": null }, { "start": 305, "end": 324, "text": "(Wang et al., 2018;", "ref_id": "BIBREF123" }, { "start": 325, "end": 347, "text": "Warstadt et al., 2019;", "ref_id": "BIBREF125" }, { "start": 348, "end": 368, "text": "Socher et al., 2013;", "ref_id": "BIBREF105" }, { "start": 369, "end": 394, "text": "Dolan and Brockett, 2005;", "ref_id": "BIBREF21" }, { "start": 395, "end": 412, "text": "Cer et al., 2017;", "ref_id": "BIBREF1" }, { "start": 413, "end": 435, "text": "Williams et al., 2018;", "ref_id": "BIBREF126" }, { "start": 436, "end": 459, "text": "Rajpurkar et al., 2018;", "ref_id": "BIBREF77" }, { "start": 460, "end": 484, "text": "Bentivogli et al., 2009;", "ref_id": null }, { "start": 485, "end": 507, "text": "Levesque et al., 2012)", "ref_id": "BIBREF42" }, { "start": 547, "end": 570, "text": "(Silveira et al., 2014)", "ref_id": "BIBREF103" } ], "ref_spans": [], "eq_spans": [], "section": "Hyperparameter Tuning", "sec_num": "4" }, { "text": "As a starting point, we evaluate single task models to ensure our implementations are competitive with the state-of-the-art. We report scores on dependency parsing (EWT), the GLUE classification tasks, and machine translation (WMT14 DE-EN (Bojar et al., 2014), IWSLT15 EN-VI (Cettolo et al., 2014) ) using mBERT as our embeddings. 10 Table 2 reports our results on the test sets compared to previous work. For all UD tasks, we score slightly higher, whereas for GLUE tasks we score consistently lower compared to the references. This is mostly due to differences in fine-tuning strategies, as implementations themselves are highly similar. Scores on the machine translation tasks show the largest drops, indicating that task-specific finetuning and pre-processing might be necessary.", "cite_spans": [ { "start": 275, "end": 297, "text": "(Cettolo et al., 2014)", "ref_id": "BIBREF3" } ], "ref_spans": [ { "start": 334, "end": 341, "text": "Table 2", "ref_id": "TABREF2" } ], "eq_spans": [], "section": "Single task evaluation", "sec_num": "5.1" }, { "text": "We evaluate the effect of a variety of multi-dataset settings on all GLUE and UD treebanks (v2.7) on the test splits. It should be noted that the UD treebanks all have the same tasks, as opposed to GLUE. First, we jointly train on all datasets (ALL), then we attempt to improve performance on smaller sets by enabling the sampling smoothing (SMOOTHED, Section 3.2.2, we set \u03b1 = 0.5). Furthermore, we attempt to improve the performance by informing the decoder of the dataset through dataset embeddings (DATASET EMBED., Section 3.2.1) or by giving each dataset its own decoder (SEP. DECODER). Results (Table 3) show that multi-task learning is only beneficial for performance when training on the same set of tasks (i.e., UD), dataset smoothing is helpful, dataset embeddings and separate decoders do not improve upon smoothing on average. For analysis purposes, we group the UD treebanks based on training size, and also evaluate UD treebanks which have no training split (zero-shot). For the zero-shot experiments, we select a proxy parser based on word overlap of the first 10 sentences of the target test data and the source training data. 11 Results on the UD data (Table 4) show that multi-task learning is mostly beneficial for mediumsized datasets (<1k and <10k). For these datasets, the combination of smoothing and dataset embeddings are the most promising settings. Perhaps surprisingly, the zero-shot datasets (<1k) have a higher LAS as compared to the small datasets and using a separate decoder based on the proxy treebank is the best setting; this is mainly because for many small datasets there is no other in-language training treebank. For the GLUE tasks (Table 5 , Appendix), multi-task learning is only beneficial for the RTE data. This is to be expected, as the tasks are different in this setup, and training data is generally larger. Dataset smoothing here prevents the model from dropping too much in performance, as it outperforms ALL for 7 out of 9 tasks.", "cite_spans": [], "ref_spans": [ { "start": 600, "end": 609, "text": "(Table 3)", "ref_id": "TABREF4" }, { "start": 1169, "end": 1178, "text": "(Table 4)", "ref_id": null }, { "start": 1672, "end": 1680, "text": "(Table 5", "ref_id": "TABREF7" } ], "eq_spans": [], "section": "Multi-dataset evaluation", "sec_num": "5.2" }, { "text": "We introduced MACHAMP, a powerful toolkit for multi-task learning supporting a wide range of NLP tasks. We also provide initial experiments demonstrating the usefulness of some of its options. We learned that multi-task learning is mostly beneficial for setups in which multiple datasets are annotated for the same set of tasks, and that dataset embeddings can still be useful when employing contextualized embeddings. However, the current experiments are just scratching the surface of MACHAMP's capabilities, as a wide variety of tasks and multi-task settings is supported. (Dirix et al., 2017) -33,894 86.7 85.9 86.6 87.0 85.9 aii as (Yako, 2019) et ewt 0 9.7 3.5 3.9 5.1 3.4 ajp madar (Zahra, 2020) ar padt 0 31.2 33.8 33.1 33.2 31.2 akk pisandub (Kopacewicz, 2018) et edt 0 3.0 4.3 4.7 3.6 3.3 akk riao (Luukko et al., 2020) et edt 0 4.0 8.2 7.6 7.3 8.1 am att (Seyoum et al., 2018) et ewt 0 1.8 0.8 0.8 0.5 0.8 apu ufpa (Freitas, 2017) fi ftb 0 6.1 13.3 13.1 8.1 13.4 aqz tudet (Aragon, 2018) cs pdt 0 6.7 9.6 9.6 9.2 14.7 ar padt (Haji\u010d et al., 2009) -191,869 31.5 31.4 31.3 31.4 31.5 ar pud (McDonald et al., 2013) ar padt 0 62.8 64.5 63.9 64.0 64.7 be hse -23,089 80.9 87.0 86.5 85.4 87.1 fo oft fo farpahc 0 49.8 62.1 62.2 61.6 62.7 fr fqb (Seddah and Candito, 2016) fr gsd 0 84.9 84.6 84.6 85.2 85.2 fr gsd (Guillaume et al., (Thomas, 2019) it isdt 0 7.7 10.5 11.1 9.2 10.9 gv cadhan (Scannell, 2020) (McDonald et al., 2013) ru syntagrus 0 86.8 88.5 89.0 86.9 87.4 ru syntagrus (Droganova et al., 2018) -870,479 93.7 93.0 88.9 92.0 93.5 ru taiga (Shavrina and Shapovalova, 2017) -43,557 77.9 78.7 79.6 81.0 80.1 sa ufal (Dwivedi and Easha, 2017) hi hdtb 0 14. (Dobrovoljc and Nivre, 2016) -19,473 69.4 73.6 74.7 73.9 73.5 sme giella (Tyers and Sheyanova, 2017) -16,835 61.3 65.3 68.5 64.5 65.5 sms giellagas (Rueter and Partanen, 2019) id gsd 0 7.8 14.9 14.6 11.7 14.8 soj aha (Mojiri et al., 2020) fa perdt 0 27.9 37.6 27.3 32.1 39.4 sq tsa (Toska et al., 2020) ga idt 0 52.1 62.8 64.0 51.2 62.6 sr set (Samard\u017ei\u0107 et al., 2017) -74,259 91.9 91.4 91.9 92.4 92.5 sv lines C\u00f6ltekin, 2020) en singpar 0 34.8 32.9 29.9 25.0 32.4 tl ugnayan (Aquino et al., 2020) en singpar 0 28.4 24.9 25.0 19.3 27.4 tpn tudet (Gerardi, 2020) cs pdt 0 9.7 5.1 4.2 6.5 3.2 tr boun (T\u00fcrk et al., 2020) -97,257 69.6 68.8 67.1 69.9 70.0 tr gb (C\u00f6ltekin, 2015) tr boun 0 66.3 64.8 64.1 66.1 66.6 tr imst (Sulubacak et al., 2016 (Wong et al., 2017) zh gsd 0 31.8 32.4 32.5 31.7 32.7 zh cfl (Lee et al., 2017) zh gsdsimp 0 47.4 48.1 47.6 46.9 47.9 zh gsd (Shen et al., 2016 (Wong et al., 2017) zh gsd 0 52.1 53.7 53.5 52.9 53.6 zh pud (McDonald et al., 2013) zh gsd 0 62.1 62.2 62.0 61.7 62.3 de tweede (Rehbein et al., 2019) -5,752 68.2 76.9 77.6 79.6 77.7 en aae (Blodgett et al., 2018) en ewt 0 51.5 55.1 55.9 56.5 56.1 en convbank (Davidson et al., 2019) -5,057 69.1 71.4 70.4 71.2 71.9 en esl (Berzak et al., 2016) -78,541 92.0 91.4 91.3 92.1 91.7 en gumreddit (Behzad and Zeldes, 2020) -10,831 75.9 84.9 84.8 86.5 85.5 en monoise (van der Goot and van Noord, 2018) en ewt 0 55.6 64.7 64.5 62.4 64.7 en singpar (Wang et al., 2019) -27,368 80.3 79.0 78.5 82.2 79.4 en tweebank2 -24,753 80.5 81.7 82.4 82.6 81.6 fr extremeugc (Mart\u00ednez Alonso et al., 2016) fr Table 6 : LAS scores from official conll2018 script on test splits of all UD datasets we could obtain, averaged over 3 random seeds. Size refers to number of sentences in the training split. Results for single dataset trained models, and our 4 multi-task strategies. The last 12 rows contain datasets that are either available without words on the official Universal Dependencies website or are not officialy submitted.", "cite_spans": [ { "start": 576, "end": 596, "text": "(Dirix et al., 2017)", "ref_id": "BIBREF18" }, { "start": 637, "end": 649, "text": "(Yako, 2019)", "ref_id": null }, { "start": 689, "end": 702, "text": "(Zahra, 2020)", "ref_id": null }, { "start": 751, "end": 769, "text": "(Kopacewicz, 2018)", "ref_id": null }, { "start": 808, "end": 829, "text": "(Luukko et al., 2020)", "ref_id": "BIBREF46" }, { "start": 866, "end": 887, "text": "(Seyoum et al., 2018)", "ref_id": "BIBREF99" }, { "start": 926, "end": 941, "text": "(Freitas, 2017)", "ref_id": "BIBREF29" }, { "start": 984, "end": 998, "text": "(Aragon, 2018)", "ref_id": null }, { "start": 1037, "end": 1057, "text": "(Haji\u010d et al., 2009)", "ref_id": null }, { "start": 1099, "end": 1122, "text": "(McDonald et al., 2013)", "ref_id": "BIBREF56" }, { "start": 1250, "end": 1276, "text": "(Seddah and Candito, 2016)", "ref_id": "BIBREF97" }, { "start": 1318, "end": 1336, "text": "(Guillaume et al.,", "ref_id": null }, { "start": 1337, "end": 1351, "text": "(Thomas, 2019)", "ref_id": "BIBREF112" }, { "start": 1395, "end": 1411, "text": "(Scannell, 2020)", "ref_id": "BIBREF96" }, { "start": 1412, "end": 1435, "text": "(McDonald et al., 2013)", "ref_id": "BIBREF56" }, { "start": 1489, "end": 1513, "text": "(Droganova et al., 2018)", "ref_id": "BIBREF23" }, { "start": 1557, "end": 1589, "text": "(Shavrina and Shapovalova, 2017)", "ref_id": "BIBREF100" }, { "start": 1631, "end": 1656, "text": "(Dwivedi and Easha, 2017)", "ref_id": "BIBREF24" }, { "start": 1671, "end": 1699, "text": "(Dobrovoljc and Nivre, 2016)", "ref_id": "BIBREF20" }, { "start": 1744, "end": 1771, "text": "(Tyers and Sheyanova, 2017)", "ref_id": "BIBREF119" }, { "start": 1819, "end": 1846, "text": "(Rueter and Partanen, 2019)", "ref_id": "BIBREF85" }, { "start": 1888, "end": 1909, "text": "(Mojiri et al., 2020)", "ref_id": null }, { "start": 1953, "end": 1973, "text": "(Toska et al., 2020)", "ref_id": "BIBREF114" }, { "start": 2015, "end": 2039, "text": "(Samard\u017ei\u0107 et al., 2017)", "ref_id": "BIBREF91" }, { "start": 2082, "end": 2097, "text": "C\u00f6ltekin, 2020)", "ref_id": null }, { "start": 2147, "end": 2168, "text": "(Aquino et al., 2020)", "ref_id": null }, { "start": 2217, "end": 2232, "text": "(Gerardi, 2020)", "ref_id": null }, { "start": 2270, "end": 2289, "text": "(T\u00fcrk et al., 2020)", "ref_id": null }, { "start": 2329, "end": 2345, "text": "(C\u00f6ltekin, 2015)", "ref_id": "BIBREF12" }, { "start": 2389, "end": 2412, "text": "(Sulubacak et al., 2016", "ref_id": "BIBREF110" }, { "start": 2413, "end": 2432, "text": "(Wong et al., 2017)", "ref_id": "BIBREF128" }, { "start": 2474, "end": 2492, "text": "(Lee et al., 2017)", "ref_id": "BIBREF128" }, { "start": 2538, "end": 2556, "text": "(Shen et al., 2016", "ref_id": null }, { "start": 2557, "end": 2576, "text": "(Wong et al., 2017)", "ref_id": "BIBREF128" }, { "start": 2618, "end": 2641, "text": "(McDonald et al., 2013)", "ref_id": "BIBREF56" }, { "start": 2686, "end": 2708, "text": "(Rehbein et al., 2019)", "ref_id": "BIBREF81" }, { "start": 2748, "end": 2771, "text": "(Blodgett et al., 2018)", "ref_id": null }, { "start": 2818, "end": 2841, "text": "(Davidson et al., 2019)", "ref_id": "BIBREF14" }, { "start": 2881, "end": 2902, "text": "(Berzak et al., 2016)", "ref_id": null }, { "start": 3019, "end": 3053, "text": "(van der Goot and van Noord, 2018)", "ref_id": "BIBREF36" }, { "start": 3099, "end": 3118, "text": "(Wang et al., 2019)", "ref_id": "BIBREF124" }, { "start": 3212, "end": 3242, "text": "(Mart\u00ednez Alonso et al., 2016)", "ref_id": "BIBREF55" } ], "ref_spans": [ { "start": 3246, "end": 3253, "text": "Table 6", "ref_id": null } ], "eq_spans": [], "section": "Conclusion", "sec_num": "6" }, { "text": "The code is available at: https://github. com/machamp-nlp/machamp (v0.2), and an instructional video at https://www.youtube.com/watch? v=DauTEdMhUDI.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "This includes both the pre-tokenization (in the traditional sense) and the subword segmentation.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "The MLM task does not require annotation, thus a raw text file can be provided.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "We do not identify comments based on lines starting with a '#', because datasets might have tokens that begin with '#'.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "These are called treebank embeddings in their work. We will use the more general term \"dataset embeddings\", which would often roughly correspond to languages and/or domains/genres.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "https://huggingface.co/models", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "Compared to MACHAMP v0.1 (van der Goot et al., 2020) we removed parameters with negligible effects (word dropout, layer dropout, adaptive softmax, and layer attention).8 https://github.com/google-research/ bert/blob/master/multilingual.md9 We capped the dataset sizes to a maximum of 20,000 sentences for efficiency reasons.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "For the sake of comparison we use BERT-large for GLUE, and EWT version 2.2.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "Scores on individual sets and proxy treebanks can be found in the Appendix.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "This is why the scores for some datasets might seem low compared to previous work, which did either do tokenization or did not take it into account during evaluation. In our case the model is punished for not tokenizing.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null } ], "back_matter": [ { "text": "We would like to thank Anouck Braggaar, Max M\u00fcller-Eberstein and Kristian N\u00f8rgaard Jensen for testing development versions. Furthermore, we thank Rik van Noord for his participation in the video, and providing an early use-case for MACHAMP (van Noord et al., 2020) . This research was supported by an Amazon Research Award, an STSM in the Multi3Generation COST action (CA18231), a visit supported by COSBI, grant 9063-00077B (Danmarks Frie Forskningsfond), and Nvidia corporation for sponsoring Titan GPUs. We thank the NLPL laboratory and the HPC team at ITU for the computational resources used in this work.", "cite_spans": [ { "start": 240, "end": 264, "text": "(van Noord et al., 2020)", "ref_id": "BIBREF60" } ], "ref_spans": [], "eq_spans": [], "section": "Acknowledgments", "sec_num": null }, { "text": "Anne Abeill\u00e9, Lionel Cl\u00e9ment, and Alexandra Kinyon.2000. Building a treebank for French. In Proceedings of the Second International Conference on Language Resources and Evaluation (LREC'00), Athens, Greece. European Language Resources Association (ELRA).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "References", "sec_num": null }, { "text": "Multi-dataset evaluation on GLUE tasks Table 5 contains the per-dataset scores for the GLUE tasks for all our tested settings. Only for RTE the performance increases when using multi-task learning. Overall, smoothing helps to overcome some of the performance loss we get when training one model on all datasets simultaneously.Multi-dataset evaluation on UD treebanks Table 6 (on the next four pages) shows the LAS scores for each treebank (UD2.7) for all of our settings. We pre-processed the data with the UDconversion tools to remove all language-specific sub-labels, and the multi-word tokens and empty nodes. However, we calculate the scores against the official files for fair comparison. 12 We included as many datasets as we could find. In the top part of the table, we include all official UD datasets for which we could get the words (only UD Arabic-NYUAD and UD Japanese-BCCWJ are missing), and the last 12 treebanks are taken from other sources, some have undergone some specific preprocessing to pass the evaluation script; details about this process can be found in the repository in scripts/udExtras.", "cite_spans": [ { "start": 694, "end": 696, "text": "12", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Appendix", "sec_num": null } ], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "Constituent order in Maltese: A quantitative analysis", "authors": [ { "first": "", "middle": [], "last": "Slavom\u00edr\u010d\u00e9pl\u00f6", "suffix": "" } ], "year": 2018, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Slavom\u00edr\u010c\u00e9pl\u00f6. 2018. Constituent order in Maltese: A quantitative analysis.", "links": null }, "BIBREF1": { "ref_id": "b1", "title": "SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation", "authors": [ { "first": "Daniel", "middle": [], "last": "Cer", "suffix": "" }, { "first": "Mona", "middle": [], "last": "Diab", "suffix": "" }, { "first": "Eneko", "middle": [], "last": "Agirre", "suffix": "" }, { "first": "I\u00f1igo", "middle": [], "last": "Lopez-Gazpio", "suffix": "" }, { "first": "Lucia", "middle": [], "last": "Specia", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)", "volume": "", "issue": "", "pages": "1--14", "other_ids": { "DOI": [ "10.18653/v1/S17-2001" ] }, "num": null, "urls": [], "raw_text": "Daniel Cer, Mona Diab, Eneko Agirre, I\u00f1igo Lopez- Gazpio, and Lucia Specia. 2017. SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 1-14, Vancouver, Canada. Association for Computational Linguistics.", "links": null }, "BIBREF2": { "ref_id": "b2", "title": "Challenges of annotating a code-switching treebank", "authors": [ { "first": "\u00c7", "middle": [], "last": "Ozlem", "suffix": "" }, { "first": "", "middle": [], "last": "Agr\u0131 \u00c7\u00f6ltekin", "suffix": "" } ], "year": 2019, "venue": "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)", "volume": "", "issue": "", "pages": "82--90", "other_ids": { "DOI": [ "10.18653/v1/W19-7809" ] }, "num": null, "urls": [], "raw_text": "Ozlem \u00c7 etinoglu and \u00c7 agr\u0131 \u00c7\u00f6ltekin. 2019. Chal- lenges of annotating a code-switching treebank. In Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019), pages 82-90, Paris, France. Association for Computational Linguistics.", "links": null }, "BIBREF3": { "ref_id": "b3", "title": "The IWSLT 2014 evaluation campaign", "authors": [ { "first": "Mauro", "middle": [], "last": "Cettolo", "suffix": "" }, { "first": "Niehues", "middle": [], "last": "Jan", "suffix": "" }, { "first": "St\u00fcker", "middle": [], "last": "Sebastian", "suffix": "" }, { "first": "Luisa", "middle": [], "last": "Bentivogli", "suffix": "" }, { "first": "Roldano", "middle": [], "last": "Cattoni", "suffix": "" }, { "first": "Marcello", "middle": [], "last": "Federico", "suffix": "" } ], "year": 2014, "venue": "In International Workshop on Spoken Language Translation", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Mauro Cettolo, Niehues Jan, St\u00fcker Sebastian, Luisa Bentivogli, Roldano Cattoni, and Marcello Federico. 2014. The IWSLT 2014 evaluation campaign. In International Workshop on Spoken Language Trans- lation, Lake Tahoe, CA, USA.", "links": null }, "BIBREF4": { "ref_id": "b4", "title": "Learning phrase representations using RNN encoder-decoder for statistical machine translation", "authors": [ { "first": "Kyunghyun", "middle": [], "last": "Cho", "suffix": "" }, { "first": "Bart", "middle": [], "last": "Van Merri\u00ebnboer", "suffix": "" }, { "first": "Caglar", "middle": [], "last": "Gulcehre", "suffix": "" }, { "first": "Dzmitry", "middle": [], "last": "Bahdanau", "suffix": "" }, { "first": "Fethi", "middle": [], "last": "Bougares", "suffix": "" }, { "first": "Holger", "middle": [], "last": "Schwenk", "suffix": "" }, { "first": "Yoshua", "middle": [], "last": "Bengio", "suffix": "" } ], "year": 2014, "venue": "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)", "volume": "", "issue": "", "pages": "1724--1734", "other_ids": { "DOI": [ "10.3115/v1/D14-1179" ] }, "num": null, "urls": [], "raw_text": "Kyunghyun Cho, Bart van Merri\u00ebnboer, Caglar Gul- cehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Nat- ural Language Processing (EMNLP), pages 1724- 1734, Doha, Qatar. Association for Computational Linguistics.", "links": null }, "BIBREF5": { "ref_id": "b5", "title": "Learning language through pictures", "authors": [ { "first": "Grzegorz", "middle": [], "last": "Chrupa\u0142a", "suffix": "" }, { "first": "\u00c1kos", "middle": [], "last": "K\u00e1d\u00e1r", "suffix": "" }, { "first": "Afra", "middle": [], "last": "Alishahi", "suffix": "" } ], "year": 2015, "venue": "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing", "volume": "2", "issue": "", "pages": "112--118", "other_ids": { "DOI": [ "10.3115/v1/P15-2019" ] }, "num": null, "urls": [], "raw_text": "Grzegorz Chrupa\u0142a,\u00c1kos K\u00e1d\u00e1r, and Afra Alishahi. 2015. Learning language through pictures. In Pro- ceedings of the 53rd Annual Meeting of the Associ- ation for Computational Linguistics and the 7th In- ternational Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 112- 118, Beijing, China. Association for Computational Linguistics.", "links": null }, "BIBREF6": { "ref_id": "b6", "title": "Simple data-driven contextsensitive lemmatization", "authors": [ { "first": "Grzegorz", "middle": [], "last": "Chrupa\u0142a", "suffix": "" } ], "year": 2006, "venue": "SEPLN", "volume": "37", "issue": "", "pages": "121--127", "other_ids": {}, "num": null, "urls": [], "raw_text": "Grzegorz Chrupa\u0142a. 2006. Simple data-driven context- sensitive lemmatization. SEPLN, 37:121-127.", "links": null }, "BIBREF7": { "ref_id": "b7", "title": "On the shortest arborescence of a directed graph", "authors": [ { "first": "Yoeng-Jin", "middle": [], "last": "Chu", "suffix": "" } ], "year": 1965, "venue": "Scientia Sinica", "volume": "14", "issue": "", "pages": "1396--1400", "other_ids": {}, "num": null, "urls": [], "raw_text": "Yoeng-Jin Chu. 1965. On the shortest arborescence of a directed graph. Scientia Sinica, 14:1396-1400.", "links": null }, "BIBREF8": { "ref_id": "b8", "title": "Building Universal Dependency treebanks in Korean", "authors": [ { "first": "Jayeol", "middle": [], "last": "Chun", "suffix": "" }, { "first": "Na-Rae", "middle": [], "last": "Han", "suffix": "" }, { "first": "Jena", "middle": [ "D" ], "last": "Hwang", "suffix": "" }, { "first": "Jinho", "middle": [ "D" ], "last": "Choi", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Jayeol Chun, Na-Rae Han, Jena D. Hwang, and Jinho D. Choi. 2018. Building Universal Depen- dency treebanks in Korean. In Proceedings of the Eleventh International Conference on Language Re- sources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).", "links": null }, "BIBREF9": { "ref_id": "b9", "title": "Presenting TWITTIR\u00d2-UD: An Italian Twitter treebank in Universal Dependencies", "authors": [ { "first": "Alessandra", "middle": [ "Teresa" ], "last": "Cignarella", "suffix": "" }, { "first": "Cristina", "middle": [], "last": "Bosco", "suffix": "" }, { "first": "Paolo", "middle": [], "last": "Rosso", "suffix": "" } ], "year": 2019, "venue": "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, Syn-taxFest 2019)", "volume": "", "issue": "", "pages": "190--197", "other_ids": { "DOI": [ "10.18653/v1/W19-7723" ] }, "num": null, "urls": [], "raw_text": "Alessandra Teresa Cignarella, Cristina Bosco, and Paolo Rosso. 2019. Presenting TWITTIR\u00d2-UD: An Italian Twitter treebank in Universal Dependen- cies. In Proceedings of the Fifth International Con- ference on Dependency Linguistics (Depling, Syn- taxFest 2019), pages 190-197, Paris, France. Asso- ciation for Computational Linguistics.", "links": null }, "BIBREF10": { "ref_id": "b10", "title": "BAM! born-again multi-task networks for natural language understanding", "authors": [ { "first": "Kevin", "middle": [], "last": "Clark", "suffix": "" }, { "first": "Minh-Thang", "middle": [], "last": "Luong", "suffix": "" }, { "first": "Urvashi", "middle": [], "last": "Khandelwal", "suffix": "" }, { "first": "Christopher", "middle": [ "D" ], "last": "Manning", "suffix": "" }, { "first": "V", "middle": [], "last": "Quoc", "suffix": "" }, { "first": "", "middle": [], "last": "Le", "suffix": "" } ], "year": 2019, "venue": "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", "volume": "", "issue": "", "pages": "5931--5937", "other_ids": { "DOI": [ "10.18653/v1/P19-1595" ] }, "num": null, "urls": [], "raw_text": "Kevin Clark, Minh-Thang Luong, Urvashi Khandel- wal, Christopher D. Manning, and Quoc V. Le. 2019. BAM! born-again multi-task networks for natural language understanding. In Proceedings of the 57th Annual Meeting of the Association for Computa- tional Linguistics, pages 5931-5937, Florence, Italy. Association for Computational Linguistics.", "links": null }, "BIBREF11": { "ref_id": "b11", "title": "Natural language processing (almost) from scratch", "authors": [ { "first": "Ronan", "middle": [], "last": "Collobert", "suffix": "" }, { "first": "Jason", "middle": [], "last": "Weston", "suffix": "" }, { "first": "L\u00e9on", "middle": [], "last": "Bottou", "suffix": "" }, { "first": "Michael", "middle": [], "last": "Karlen", "suffix": "" }, { "first": "Koray", "middle": [], "last": "Kavukcuoglu", "suffix": "" }, { "first": "Pavel", "middle": [], "last": "Kuksa", "suffix": "" } ], "year": 2011, "venue": "Journal of Machine Learning Research", "volume": "12", "issue": "", "pages": "2493--2537", "other_ids": {}, "num": null, "urls": [], "raw_text": "Ronan Collobert, Jason Weston, L\u00e9on Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12:2493- 2537.", "links": null }, "BIBREF12": { "ref_id": "b12", "title": "A grammar-book treebank of Turkish", "authors": [ { "first": "Cagr\u0131", "middle": [], "last": "C\u00f6ltekin", "suffix": "" } ], "year": 2015, "venue": "Proceedings of the 14th workshop on Treebanks and Linguistic Theories (TLT 14)", "volume": "", "issue": "", "pages": "35--49", "other_ids": {}, "num": null, "urls": [], "raw_text": "Cagr\u0131 C\u00f6ltekin. 2015. A grammar-book treebank of Turkish. In Proceedings of the 14th workshop on Treebanks and Linguistic Theories (TLT 14), pages 35-49.", "links": null }, "BIBREF13": { "ref_id": "b13", "title": "Crosslingual language model pretraining", "authors": [ { "first": "Alexis", "middle": [], "last": "Conneau", "suffix": "" }, { "first": "Guillaume", "middle": [], "last": "Lample", "suffix": "" } ], "year": 2019, "venue": "Advances in Neural Information Processing Systems", "volume": "", "issue": "", "pages": "7059--7069", "other_ids": {}, "num": null, "urls": [], "raw_text": "Alexis Conneau and Guillaume Lample. 2019. Cross- lingual language model pretraining. In Advances in Neural Information Processing Systems, pages 7059-7069, Vancouver, Canada.", "links": null }, "BIBREF14": { "ref_id": "b14", "title": "Dependency parsing for spoken dialog systems", "authors": [ { "first": "Sam", "middle": [], "last": "Davidson", "suffix": "" }, { "first": "Dian", "middle": [], "last": "Yu", "suffix": "" }, { "first": "Zhou", "middle": [], "last": "Yu", "suffix": "" } ], "year": 2019, "venue": "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", "volume": "", "issue": "", "pages": "1513--1519", "other_ids": { "DOI": [ "10.18653/v1/D19-1162" ] }, "num": null, "urls": [], "raw_text": "Sam Davidson, Dian Yu, and Zhou Yu. 2019. De- pendency parsing for spoken dialog systems. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Lan- guage Processing (EMNLP-IJCNLP), pages 1513- 1519, Hong Kong, China. Association for Computa- tional Linguistics.", "links": null }, "BIBREF15": { "ref_id": "b15", "title": "UD Old Turkish-Tonqq", "authors": [ { "first": "Mehmet", "middle": [], "last": "Oguz Derin", "suffix": "" } ], "year": 2020, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Mehmet Oguz Derin. 2020. UD Old Turkish- Tonqq. https://github.com/ UniversalDependencies/UD_Old_ Turkish-Tonqq.", "links": null }, "BIBREF16": { "ref_id": "b16", "title": "BERT: Pre-training of deep bidirectional transformers for language understanding", "authors": [ { "first": "Jacob", "middle": [], "last": "Devlin", "suffix": "" }, { "first": "Ming-Wei", "middle": [], "last": "Chang", "suffix": "" }, { "first": "Kenton", "middle": [], "last": "Lee", "suffix": "" }, { "first": "Kristina", "middle": [], "last": "Toutanova", "suffix": "" } ], "year": 2019, "venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", "volume": "1", "issue": "", "pages": "4171--4186", "other_ids": { "DOI": [ "10.18653/v1/N19-1423" ] }, "num": null, "urls": [], "raw_text": "Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Associ- ation for Computational Linguistics.", "links": null }, "BIBREF17": { "ref_id": "b17", "title": "Developing Universal Dependencies for Wolof", "authors": [ { "first": "Cheikh", "middle": [], "last": "Bamba", "suffix": "" }, { "first": "Dione", "middle": [], "last": "", "suffix": "" } ], "year": 2019, "venue": "Proceedings of the Third Workshop on Universal Dependencies (UDW, Syn-taxFest 2019)", "volume": "", "issue": "", "pages": "12--23", "other_ids": { "DOI": [ "10.18653/v1/W19-8003" ] }, "num": null, "urls": [], "raw_text": "Cheikh Bamba Dione. 2019. Developing Universal De- pendencies for Wolof. In Proceedings of the Third Workshop on Universal Dependencies (UDW, Syn- taxFest 2019), pages 12-23, Paris, France. Associ- ation for Computational Linguistics.", "links": null }, "BIBREF18": { "ref_id": "b18", "title": "Universal Dependencies for Afrikaans", "authors": [ { "first": "Peter", "middle": [], "last": "Dirix", "suffix": "" }, { "first": "Liesbeth", "middle": [], "last": "Augustinus", "suffix": "" }, { "first": "Frank", "middle": [], "last": "Daniel Van Niekerk", "suffix": "" }, { "first": "", "middle": [], "last": "Van Eynde", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the NoDaLiDa 2017 Workshop on Universal Dependencies (UDW 2017)", "volume": "", "issue": "", "pages": "38--47", "other_ids": {}, "num": null, "urls": [], "raw_text": "Peter Dirix, Liesbeth Augustinus, Daniel van Niekerk, and Frank Van Eynde. 2017. Universal Dependen- cies for Afrikaans. In Proceedings of the NoDaLiDa 2017 Workshop on Universal Dependencies (UDW 2017), pages 38-47, Gothenburg, Sweden. Associa- tion for Computational Linguistics.", "links": null }, "BIBREF19": { "ref_id": "b19", "title": "The Universal Dependencies treebank for Slovenian", "authors": [ { "first": "Kaja", "middle": [], "last": "Dobrovoljc", "suffix": "" }, { "first": "Toma\u017e", "middle": [], "last": "Erjavec", "suffix": "" }, { "first": "Simon", "middle": [], "last": "Krek", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing", "volume": "", "issue": "", "pages": "33--38", "other_ids": { "DOI": [ "10.18653/v1/W17-1406" ] }, "num": null, "urls": [], "raw_text": "Kaja Dobrovoljc, Toma\u017e Erjavec, and Simon Krek. 2017. The Universal Dependencies treebank for Slovenian. In Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing, pages 33-38, Valencia, Spain. Association for Computa- tional Linguistics.", "links": null }, "BIBREF20": { "ref_id": "b20", "title": "The Universal Dependencies treebank of spoken Slovenian", "authors": [ { "first": "Kaja", "middle": [], "last": "Dobrovoljc", "suffix": "" }, { "first": "Joakim", "middle": [], "last": "Nivre", "suffix": "" } ], "year": 2016, "venue": "Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)", "volume": "", "issue": "", "pages": "1566--1573", "other_ids": {}, "num": null, "urls": [], "raw_text": "Kaja Dobrovoljc and Joakim Nivre. 2016. The Univer- sal Dependencies treebank of spoken Slovenian. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1566-1573, Portoro\u017e, Slovenia. European Language Resources Association (ELRA).", "links": null }, "BIBREF21": { "ref_id": "b21", "title": "Automatically constructing a corpus of sentential paraphrases", "authors": [ { "first": "B", "middle": [], "last": "William", "suffix": "" }, { "first": "Chris", "middle": [], "last": "Dolan", "suffix": "" }, { "first": "", "middle": [], "last": "Brockett", "suffix": "" } ], "year": 2005, "venue": "Proceedings of the Third International Workshop on Paraphrasing (IWP2005)", "volume": "", "issue": "", "pages": "9--16", "other_ids": {}, "num": null, "urls": [], "raw_text": "William B. Dolan and Chris Brockett. 2005. Automati- cally constructing a corpus of sentential paraphrases. In Proceedings of the Third International Workshop on Paraphrasing (IWP2005), pages 9-16, Jeju Is- land, Korea.", "links": null }, "BIBREF22": { "ref_id": "b22", "title": "Deep biaffine attention for neural dependency parsing", "authors": [ { "first": "Timothy", "middle": [], "last": "Dozat", "suffix": "" }, { "first": "D", "middle": [], "last": "Christopher", "suffix": "" }, { "first": "", "middle": [], "last": "Manning", "suffix": "" } ], "year": 2017, "venue": "Proceedings of 5th International Conference on Learning Representations", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Timothy Dozat and Christopher D Manning. 2017. Deep biaffine attention for neural dependency pars- ing. In Proceedings of 5th International Conference on Learning Representations, ICLR 2017, Confer- ence Track Proceedings, Toulon, France.", "links": null }, "BIBREF23": { "ref_id": "b23", "title": "Data conversion and consistency of monolingual corpora: Russian UD treebanks", "authors": [ { "first": "Kira", "middle": [], "last": "Droganova", "suffix": "" }, { "first": "Olga", "middle": [], "last": "Lyashevskaya", "suffix": "" }, { "first": "Daniel", "middle": [], "last": "Zeman", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the 17th international workshop on treebanks and linguistic theories", "volume": "155", "issue": "", "pages": "53--66", "other_ids": {}, "num": null, "urls": [], "raw_text": "Kira Droganova, Olga Lyashevskaya, and Daniel Ze- man. 2018. Data conversion and consistency of monolingual corpora: Russian UD treebanks. In Proceedings of the 17th international workshop on treebanks and linguistic theories (tlt 2018), 155, pages 53-66.", "links": null }, "BIBREF24": { "ref_id": "b24", "title": "Universal Dependencies for Sanskrit", "authors": [ { "first": "Puneet", "middle": [], "last": "Dwivedi", "suffix": "" }, { "first": "Guha", "middle": [], "last": "Easha", "suffix": "" } ], "year": 2017, "venue": "International Journal of Advance Research", "volume": "3", "issue": "4", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Puneet Dwivedi and Guha Easha. 2017. Universal De- pendencies for Sanskrit. International Journal of Advance Research, Ideas and Innovations in Tech- nology, 3(4).", "links": null }, "BIBREF25": { "ref_id": "b25", "title": "The PROIEL treebank family: a standard for early attestations of Indo-European languages", "authors": [ { "first": "Hanne", "middle": [], "last": "Eckhoff", "suffix": "" }, { "first": "Kristin", "middle": [], "last": "Bech", "suffix": "" }, { "first": "Gerlof", "middle": [], "last": "Bouma", "suffix": "" }, { "first": "Kristine", "middle": [], "last": "Eide", "suffix": "" }, { "first": "Dag", "middle": [], "last": "Haug", "suffix": "" } ], "year": 2018, "venue": "Language Resources and Evaluation", "volume": "52", "issue": "1", "pages": "29--65", "other_ids": {}, "num": null, "urls": [], "raw_text": "Hanne Eckhoff, Kristin Bech, Gerlof Bouma, Kris- tine Eide, Dag Haug, Odd Einar Haugen, and Mar- ius J\u00f8hndal. 2018. The PROIEL treebank family: a standard for early attestations of Indo-European languages. Language Resources and Evaluation, 52(1):29-65.", "links": null }, "BIBREF26": { "ref_id": "b26", "title": "Linguistics vs. digital editions: The Troms\u00f8 Old Russian and OCS treebank", "authors": [ { "first": "Martine", "middle": [], "last": "Hanne", "suffix": "" }, { "first": "Aleksandrs", "middle": [], "last": "Eckhoff", "suffix": "" }, { "first": "", "middle": [], "last": "Berdi\u010devskis", "suffix": "" } ], "year": 2015, "venue": "Scripta & e-Scripta", "volume": "14", "issue": "15", "pages": "9--25", "other_ids": {}, "num": null, "urls": [], "raw_text": "Hanne Martine Eckhoff and Aleksandrs Berdi\u010devskis. 2015. Linguistics vs. digital editions: The Troms\u00f8 Old Russian and OCS treebank. Scripta & e-Scripta, 14(15):9-25.", "links": null }, "BIBREF27": { "ref_id": "b27", "title": "Optimum branchings", "authors": [], "year": 1967, "venue": "Journal of Research of the national Bureau of Standards B", "volume": "71", "issue": "4", "pages": "233--240", "other_ids": {}, "num": null, "urls": [], "raw_text": "Jack Edmonds. 1967. Optimum branchings. Journal of Research of the national Bureau of Standards B, 71(4):233-240.", "links": null }, "BIBREF28": { "ref_id": "b28", "title": "Universal dependencies for Uyghur", "authors": [ { "first": "Marhaba", "middle": [], "last": "Eli", "suffix": "" }, { "first": "Weinila", "middle": [], "last": "Mushajiang", "suffix": "" }, { "first": "Tuergen", "middle": [], "last": "Yibulayin", "suffix": "" }, { "first": "Kahaerjiang", "middle": [], "last": "Abiderexiti", "suffix": "" }, { "first": "Yan", "middle": [], "last": "Liu", "suffix": "" } ], "year": 2016, "venue": "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies (WLSI/OIAF4HLT2016)", "volume": "", "issue": "", "pages": "44--50", "other_ids": {}, "num": null, "urls": [], "raw_text": "Marhaba Eli, Weinila Mushajiang, Tuergen Yibulayin, Kahaerjiang Abiderexiti, and Yan Liu. 2016. Uni- versal dependencies for Uyghur. In Proceedings of the Third International Workshop on World- wide Language Service Infrastructure and Sec- ond Workshop on Open Infrastructures and Anal- ysis Frameworks for Human Language Technolo- gies (WLSI/OIAF4HLT2016), pages 44-50, Osaka, Japan. The COLING 2016 Organizing Committee.", "links": null }, "BIBREF29": { "ref_id": "b29", "title": "A posse em apurin\u00e3: Descri\u00e7\u00e3o de constru\u00e7\u00f5es atributivas e predicativas em compara\u00e7\u00e3o com outras l\u00ednguas aru\u00e1k. Bel\u00e9m: Programa de P\u00f3s-Gradua\u00e7\u00e3o em Letras", "authors": [ { "first": "Mar\u00edlia Fernanda Pereira De", "middle": [], "last": "Freitas", "suffix": "" } ], "year": 2017, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Mar\u00edlia Fernanda Pereira de Freitas. 2017. A posse em apurin\u00e3: Descri\u00e7\u00e3o de constru\u00e7\u00f5es atributivas e pred- icativas em compara\u00e7\u00e3o com outras l\u00ednguas aru\u00e1k. Bel\u00e9m: Programa de P\u00f3s-Gradua\u00e7\u00e3o em Letras, Universidade Federal do Par\u00e1 (Tese de Doutorado).", "links": null }, "BIBREF30": { "ref_id": "b30", "title": "Universal dependencies guidelines for the Galician-TreeGal treebank", "authors": [ { "first": "Marcos", "middle": [], "last": "Garcia", "suffix": "" } ], "year": 2016, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Marcos Garcia. 2016. Universal dependencies guide- lines for the Galician-TreeGal treebank. Technical report, Technical Report, LyS Group, Universidade da Coruna.", "links": null }, "BIBREF31": { "ref_id": "b31", "title": "AllenNLP: A deep semantic natural language processing platform", "authors": [ { "first": "Matt", "middle": [], "last": "Gardner", "suffix": "" }, { "first": "Joel", "middle": [], "last": "Grus", "suffix": "" }, { "first": "Mark", "middle": [], "last": "Neumann", "suffix": "" }, { "first": "Oyvind", "middle": [], "last": "Tafjord", "suffix": "" }, { "first": "Pradeep", "middle": [], "last": "Dasigi", "suffix": "" }, { "first": "Nelson", "middle": [ "F" ], "last": "Liu", "suffix": "" }, { "first": "Matthew", "middle": [], "last": "Peters", "suffix": "" }, { "first": "Michael", "middle": [], "last": "Schmitz", "suffix": "" }, { "first": "Luke", "middle": [], "last": "Zettlemoyer", "suffix": "" } ], "year": 2018, "venue": "Proceedings of Workshop for NLP Open Source Software (NLP-OSS)", "volume": "", "issue": "", "pages": "1--6", "other_ids": { "DOI": [ "10.18653/v1/W18-2501" ] }, "num": null, "urls": [], "raw_text": "Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Pe- ters, Michael Schmitz, and Luke Zettlemoyer. 2018. AllenNLP: A deep semantic natural language pro- cessing platform. In Proceedings of Workshop for NLP Open Source Software (NLP-OSS), pages 1- 6, Melbourne, Australia. Association for Computa- tional Linguistics.", "links": null }, "BIBREF33": { "ref_id": "b33", "title": "The structure of Munduruk\u00fa", "authors": [ { "first": "Gerardi", "middle": [], "last": "Fabr\u00eccio Ferraz", "suffix": "" } ], "year": 2021, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Fabr\u00eccio Ferraz Gerardi. 2021. The structure of Munduruk\u00fa.", "links": null }, "BIBREF34": { "ref_id": "b34", "title": "A dependency treebank for Kurmanji Kurdish", "authors": [ { "first": "Memduh", "middle": [], "last": "G\u00f6k\u0131rmak", "suffix": "" }, { "first": "Francis", "middle": [ "M" ], "last": "Tyers", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the Fourth International Conference on Dependency Linguistics", "volume": "", "issue": "", "pages": "64--72", "other_ids": {}, "num": null, "urls": [], "raw_text": "Memduh G\u00f6k\u0131rmak and Francis M. Tyers. 2017. A dependency treebank for Kurmanji Kurdish. In Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017), pages 64-72, Pisa,Italy. Link\u00f6ping University Electronic Press.", "links": null }, "BIBREF35": { "ref_id": "b35", "title": "Recursos integrados da lingua galega para a investigaci\u00f3n ling\u00fc\u00edstica. Gallaecia. Estudos de ling\u00fc\u00edstica portuguesa e galega", "authors": [ { "first": "Guinovart", "middle": [], "last": "Xavier G\u00f3mez", "suffix": "" } ], "year": 2017, "venue": "", "volume": "", "issue": "", "pages": "1037--1048", "other_ids": {}, "num": null, "urls": [], "raw_text": "Xavier G\u00f3mez Guinovart. 2017. Recursos integra- dos da lingua galega para a investigaci\u00f3n ling\u00fc\u00edstica. Gallaecia. Estudos de ling\u00fc\u00edstica portuguesa e galega. Santiago de Compostela: Universidade de Santiago, pages 1037-1048.", "links": null }, "BIBREF36": { "ref_id": "b36", "title": "Modeling input uncertainty in neural network dependency parsing", "authors": [ { "first": "Rob", "middle": [], "last": "Van Der Goot", "suffix": "" }, { "first": "Gertjan", "middle": [], "last": "Van Noord", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", "volume": "", "issue": "", "pages": "4984--4991", "other_ids": { "DOI": [ "10.18653/v1/D18-1542" ] }, "num": null, "urls": [], "raw_text": "Rob van der Goot and Gertjan van Noord. 2018. Mod- eling input uncertainty in neural network depen- dency parsing. In Proceedings of the 2018 Con- ference on Empirical Methods in Natural Language Processing, pages 4984-4991, Brussels, Belgium. Association for Computational Linguistics.", "links": null }, "BIBREF37": { "ref_id": "b37", "title": "Massive choice, ample tasks (MaChAmp): A toolkit for multi-task learning in NLP", "authors": [ { "first": "Rob", "middle": [], "last": "Van Der Goot", "suffix": "" }, { "first": "Alan", "middle": [], "last": "Ahmet\u00fcst\u00fcn", "suffix": "" }, { "first": "Barbara", "middle": [], "last": "Ramponi", "suffix": "" }, { "first": "", "middle": [], "last": "Plank", "suffix": "" } ], "year": 2020, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": { "arXiv": [ "arXiv:2005.14672v2" ] }, "num": null, "urls": [], "raw_text": "Rob van der Goot, Ahmet\u00dcst\u00fcn, Alan Ramponi, and Barbara Plank. 2020. Massive choice, ample tasks (MaChAmp): A toolkit for multi-task learning in NLP. arXiv preprint arXiv:2005.14672v2.", "links": null }, "BIBREF38": { "ref_id": "b38", "title": "Creation of a balanced state-of-the-art multilayer corpus for NLU", "authors": [ { "first": "Normunds", "middle": [], "last": "Gruzitis", "suffix": "" }, { "first": "Lauma", "middle": [], "last": "Pretkalnina", "suffix": "" }, { "first": "Baiba", "middle": [], "last": "Saulite", "suffix": "" }, { "first": "Laura", "middle": [], "last": "Rituma", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Normunds Gruzitis, Lauma Pretkalnina, Baiba Saulite, Laura Rituma, Gunta Nespore-Berzkalne, Arturs Znotins, and Peteris Paikens. 2018. Creation of a balanced state-of-the-art multilayer corpus for NLU. In Proceedings of the Eleventh International Confer- ence on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Re- sources Association (ELRA).", "links": null }, "BIBREF39": { "ref_id": "b39", "title": "Conversion et am\u00e9liorations de corpus du Fran\u00e7ais annot\u00e9s en Universal Dependencies", "authors": [ { "first": "Bruno", "middle": [], "last": "Guillaume", "suffix": "" }, { "first": "Marie-Catherine", "middle": [], "last": "De Marneffe", "suffix": "" }, { "first": "Guy", "middle": [], "last": "Perrier", "suffix": "" } ], "year": 2019, "venue": "Traitement Automatique des Langues", "volume": "60", "issue": "2", "pages": "71--95", "other_ids": {}, "num": null, "urls": [], "raw_text": "Bruno Guillaume, Marie-Catherine de Marneffe, and Guy Perrier. 2019. Conversion et am\u00e9liorations de corpus du Fran\u00e7ais annot\u00e9s en Universal De- pendencies. Traitement Automatique des Langues, 60(2):71-95.", "links": null }, "BIBREF41": { "ref_id": "b41", "title": "Association for Computational Linguistics", "authors": [], "year": 2017, "venue": "", "volume": "", "issue": "", "pages": "67--71", "other_ids": {}, "num": null, "urls": [], "raw_text": "on Universal Dependencies (UDW 2017), pages 67- 71, Gothenburg, Sweden. Association for Computa- tional Linguistics.", "links": null }, "BIBREF42": { "ref_id": "b42", "title": "The Winograd schema challenge", "authors": [ { "first": "Hector", "middle": [], "last": "Levesque", "suffix": "" }, { "first": "Ernest", "middle": [], "last": "Davis", "suffix": "" }, { "first": "Leora", "middle": [], "last": "Morgenstern", "suffix": "" } ], "year": 2012, "venue": "Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Hector Levesque, Ernest Davis, and Leora Morgen- stern. 2012. The Winograd schema challenge. In Thirteenth International Conference on the Princi- ples of Knowledge Representation and Reasoning, Rome, Italy.", "links": null }, "BIBREF43": { "ref_id": "b43", "title": "Very deep transformers for neural machine translation", "authors": [ { "first": "Xiaodong", "middle": [], "last": "Liu", "suffix": "" }, { "first": "Kevin", "middle": [], "last": "Duh", "suffix": "" }, { "first": "Liyuan", "middle": [], "last": "Liu", "suffix": "" }, { "first": "Jianfeng", "middle": [], "last": "Gao", "suffix": "" } ], "year": 2020, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": { "arXiv": [ "arXiv:2008.07772v2" ] }, "num": null, "urls": [], "raw_text": "Xiaodong Liu, Kevin Duh, Liyuan Liu, and Jian- feng Gao. 2020. Very deep transformers for neural machine translation. arXiv preprint arXiv:2008.07772v2.", "links": null }, "BIBREF44": { "ref_id": "b44", "title": "Parsing tweets into Universal Dependencies", "authors": [ { "first": "Yijia", "middle": [], "last": "Liu", "suffix": "" }, { "first": "Yi", "middle": [], "last": "Zhu", "suffix": "" }, { "first": "Wanxiang", "middle": [], "last": "Che", "suffix": "" }, { "first": "Bing", "middle": [], "last": "Qin", "suffix": "" }, { "first": "Nathan", "middle": [], "last": "Schneider", "suffix": "" }, { "first": "Noah", "middle": [ "A" ], "last": "Smith", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", "volume": "1", "issue": "", "pages": "965--975", "other_ids": { "DOI": [ "10.18653/v1/N18-1088" ] }, "num": null, "urls": [], "raw_text": "Yijia Liu, Yi Zhu, Wanxiang Che, Bing Qin, Nathan Schneider, and Noah A. Smith. 2018. Parsing tweets into Universal Dependencies. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Hu- man Language Technologies, Volume 1 (Long Pa- pers), pages 965-975, New Orleans, Louisiana. As- sociation for Computational Linguistics.", "links": null }, "BIBREF45": { "ref_id": "b45", "title": "RoBERTa: A robustly optimized BERT pretraining approach", "authors": [ { "first": "Yinhan", "middle": [], "last": "Liu", "suffix": "" }, { "first": "Myle", "middle": [], "last": "Ott", "suffix": "" }, { "first": "Naman", "middle": [], "last": "Goyal", "suffix": "" }, { "first": "Jingfei", "middle": [], "last": "Du", "suffix": "" }, { "first": "Mandar", "middle": [], "last": "Joshi", "suffix": "" }, { "first": "Danqi", "middle": [], "last": "Chen", "suffix": "" }, { "first": "Omer", "middle": [], "last": "Levy", "suffix": "" }, { "first": "Mike", "middle": [], "last": "Lewis", "suffix": "" }, { "first": "Luke", "middle": [], "last": "Zettlemoyer", "suffix": "" }, { "first": "Veselin", "middle": [], "last": "Stoyanov", "suffix": "" } ], "year": 2019, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": { "arXiv": [ "arXiv:1907.11692" ] }, "num": null, "urls": [], "raw_text": "Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692.", "links": null }, "BIBREF46": { "ref_id": "b46", "title": "Akkadian treebank for early neo-assyrian royal inscriptions", "authors": [ { "first": "Mikko", "middle": [], "last": "Luukko", "suffix": "" }, { "first": "Aleksi", "middle": [], "last": "Sahala", "suffix": "" }, { "first": "Sam", "middle": [], "last": "Hardwick", "suffix": "" }, { "first": "Krister", "middle": [], "last": "Lind\u00e9n", "suffix": "" } ], "year": 2020, "venue": "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories", "volume": "", "issue": "", "pages": "124--134", "other_ids": { "DOI": [ "10.18653/v1/2020.tlt-1.11" ] }, "num": null, "urls": [], "raw_text": "Mikko Luukko, Aleksi Sahala, Sam Hardwick, and Krister Lind\u00e9n. 2020. Akkadian treebank for early neo-assyrian royal inscriptions. In Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories, pages 124-134, D\u00fcsseldorf, Germany. Association for Computational Linguis- tics.", "links": null }, "BIBREF47": { "ref_id": "b47", "title": "A reusable tagset for the morphologically rich language in change: A case of Middle Russian", "authors": [ { "first": "Olga", "middle": [], "last": "Lyashevskaya", "suffix": "" } ], "year": 2019, "venue": "Proceedings of the International Conference Dialogue", "volume": "", "issue": "", "pages": "422--434", "other_ids": {}, "num": null, "urls": [], "raw_text": "Olga Lyashevskaya. 2019. A reusable tagset for the morphologically rich language in change: A case of Middle Russian. In Proceedings of the International Conference Dialogue 2019, pages 422-434.", "links": null }, "BIBREF50": { "ref_id": "b50", "title": "Universal dependencies for irish", "authors": [ { "first": "Teresa", "middle": [], "last": "Lynn", "suffix": "" }, { "first": "Jennifer", "middle": [], "last": "Foster", "suffix": "" } ], "year": 2016, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Teresa Lynn and Jennifer Foster. 2016. Universal de- pendencies for irish. In CLTW.", "links": null }, "BIBREF51": { "ref_id": "b51", "title": "Syntactic annotation of Kazakh: Following the Universal Dependencies guidelines. a report", "authors": [ { "first": "Aibek", "middle": [], "last": "Makazhanov", "suffix": "" }, { "first": "Aitolkyn", "middle": [], "last": "Sultangazina", "suffix": "" }, { "first": "Olzhas", "middle": [], "last": "Makhambetov", "suffix": "" }, { "first": "Zhandos", "middle": [], "last": "Yessenbayev", "suffix": "" } ], "year": 2015, "venue": "3rd International Conference on Turkic Languages Processing", "volume": "", "issue": "", "pages": "338--350", "other_ids": {}, "num": null, "urls": [], "raw_text": "Aibek Makazhanov, Aitolkyn Sultangazina, Olzhas Makhambetov, and Zhandos Yessenbayev. 2015. Syntactic annotation of Kazakh: Following the Uni- versal Dependencies guidelines. a report. In 3rd International Conference on Turkic Languages Pro- cessing, (TurkLang 2015), pages 338-350.", "links": null }, "BIBREF52": { "ref_id": "b52", "title": "Computational linguistics and deep learning", "authors": [ { "first": "D", "middle": [], "last": "Christopher", "suffix": "" }, { "first": "", "middle": [], "last": "Manning", "suffix": "" } ], "year": 2015, "venue": "Computational Linguistics", "volume": "41", "issue": "4", "pages": "701--707", "other_ids": { "DOI": [ "10.1162/COLI_a_00239" ] }, "num": null, "urls": [], "raw_text": "Christopher D Manning. 2015. Computational linguis- tics and deep learning. Computational Linguistics, 41(4):701-707.", "links": null }, "BIBREF53": { "ref_id": "b53", "title": "Social media-processing Romanian chat and discourse analysis", "authors": [ { "first": "C\u0203t\u0203lina", "middle": [], "last": "M\u0203r\u0203nduc", "suffix": "" }, { "first": "Cenel-Augusto", "middle": [], "last": "Perez", "suffix": "" }, { "first": "Radu", "middle": [], "last": "Simionescu", "suffix": "" } ], "year": 2016, "venue": "Computaci\u00f3n y Sistemas", "volume": "20", "issue": "3", "pages": "405--414", "other_ids": {}, "num": null, "urls": [], "raw_text": "C\u0203t\u0203lina M\u0203r\u0203nduc, Cenel-Augusto Perez, and Radu Simionescu. 2016. Social media-processing Roma- nian chat and discourse analysis. Computaci\u00f3n y Sis- temas, 20(3):405-414.", "links": null }, "BIBREF54": { "ref_id": "b54", "title": "When is multitask learning effective? semantic sequence prediction under varying data conditions", "authors": [ { "first": "Alonso", "middle": [], "last": "H\u00e9ctor Mart\u00ednez", "suffix": "" }, { "first": "Barbara", "middle": [], "last": "Plank", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics", "volume": "1", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "H\u00e9ctor Mart\u00ednez Alonso and Barbara Plank. 2017. When is multitask learning effective? semantic se- quence prediction under varying data conditions. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Lin- guistics: Volume 1, Long Papers, Valencia, Spain. Association for Computational Linguistics.", "links": null }, "BIBREF55": { "ref_id": "b55", "title": "From noisy questions to Minecraft texts: Annotation challenges in extreme syntax scenario", "authors": [ { "first": "Djam\u00e9", "middle": [], "last": "H\u00e9ctor Mart\u00ednez Alonso", "suffix": "" }, { "first": "Beno\u00eet", "middle": [], "last": "Seddah", "suffix": "" }, { "first": "", "middle": [], "last": "Sagot", "suffix": "" } ], "year": 2016, "venue": "Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)", "volume": "", "issue": "", "pages": "13--23", "other_ids": {}, "num": null, "urls": [], "raw_text": "H\u00e9ctor Mart\u00ednez Alonso, Djam\u00e9 Seddah, and Beno\u00eet Sagot. 2016. From noisy questions to Minecraft texts: Annotation challenges in extreme syntax sce- nario. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 13-23, Osaka, Japan. The COLING 2016 Organizing Committee.", "links": null }, "BIBREF56": { "ref_id": "b56", "title": "Universal Dependency annotation for multilingual parsing", "authors": [ { "first": "Ryan", "middle": [], "last": "Mcdonald", "suffix": "" }, { "first": "Joakim", "middle": [], "last": "Nivre", "suffix": "" }, { "first": "Yvonne", "middle": [], "last": "Quirmbach-Brundage", "suffix": "" }, { "first": "Yoav", "middle": [], "last": "Goldberg", "suffix": "" }, { "first": "Dipanjan", "middle": [], "last": "Das", "suffix": "" }, { "first": "Kuzman", "middle": [], "last": "Ganchev", "suffix": "" }, { "first": "Keith", "middle": [], "last": "Hall", "suffix": "" }, { "first": "Slav", "middle": [], "last": "Petrov", "suffix": "" }, { "first": "Hao", "middle": [], "last": "Zhang", "suffix": "" }, { "first": "Oscar", "middle": [], "last": "T\u00e4ckstr\u00f6m", "suffix": "" }, { "first": "Claudia", "middle": [], "last": "Bedini", "suffix": "" }, { "first": "N\u00faria", "middle": [], "last": "Bertomeu Castell\u00f3", "suffix": "" }, { "first": "Jungmee", "middle": [], "last": "Lee", "suffix": "" } ], "year": 2013, "venue": "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics", "volume": "", "issue": "", "pages": "92--97", "other_ids": {}, "num": null, "urls": [], "raw_text": "Ryan McDonald, Joakim Nivre, Yvonne Quirmbach- Brundage, Yoav Goldberg, Dipanjan Das, Kuz- man Ganchev, Keith Hall, Slav Petrov, Hao Zhang, Oscar T\u00e4ckstr\u00f6m, Claudia Bedini, N\u00faria Bertomeu Castell\u00f3, and Jungmee Lee. 2013. Uni- versal Dependency annotation for multilingual pars- ing. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Vol- ume 2: Short Papers), pages 92-97, Sofia, Bulgaria. Association for Computational Linguistics.", "links": null }, "BIBREF57": { "ref_id": "b57", "title": "MoNERo: a biomedical gold standard corpus for the Romanian language", "authors": [ { "first": "Maria", "middle": [], "last": "Mitrofan", "suffix": "" }, { "first": "Grigorina", "middle": [], "last": "Verginica Barbu Mititelu", "suffix": "" }, { "first": "", "middle": [], "last": "Mitrofan", "suffix": "" } ], "year": 2019, "venue": "Proceedings of the 18th BioNLP Workshop and Shared Task", "volume": "", "issue": "", "pages": "71--79", "other_ids": { "DOI": [ "10.18653/v1/W19-5008" ] }, "num": null, "urls": [], "raw_text": "Maria Mitrofan, Verginica Barbu Mititelu, and Grigo- rina Mitrofan. 2019. MoNERo: a biomedical gold standard corpus for the Romanian language. In Pro- ceedings of the 18th BioNLP Workshop and Shared Task, pages 71-79, Florence, Italy. Association for Computational Linguistics.", "links": null }, "BIBREF58": { "ref_id": "b58", "title": "Human-in-the-loop machine learning", "authors": [ { "first": "Robert", "middle": [], "last": "Munro", "suffix": "" } ], "year": 2020, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Robert Munro. 2020. Human-in-the-loop machine learning. Sl: O'REILLY MEDIA.", "links": null }, "BIBREF59": { "ref_id": "b59", "title": "Building a large syntacticallyannotated corpus of Vietnamese", "authors": [ { "first": "Phuong-Thai", "middle": [], "last": "Nguyen", "suffix": "" }, { "first": "Xuan-Luong", "middle": [], "last": "Vu", "suffix": "" }, { "first": "Thi-Minh-Huyen", "middle": [], "last": "Nguyen", "suffix": "" }, { "first": "Hong-Phuong", "middle": [], "last": "Van-Hiep Nguyen", "suffix": "" }, { "first": "", "middle": [], "last": "Le", "suffix": "" } ], "year": 2009, "venue": "Proceedings of the Third Linguistic Annotation Workshop (LAW III)", "volume": "", "issue": "", "pages": "182--185", "other_ids": {}, "num": null, "urls": [], "raw_text": "Phuong-Thai Nguyen, Xuan-Luong Vu, Thi-Minh- Huyen Nguyen, Van-Hiep Nguyen, and Hong- Phuong Le. 2009. Building a large syntactically- annotated corpus of Vietnamese. In Proceedings of the Third Linguistic Annotation Workshop (LAW III), pages 182-185, Suntec, Singapore. Association for Computational Linguistics.", "links": null }, "BIBREF60": { "ref_id": "b60", "title": "Character-level representations improve DRS-based semantic parsing Even in the age of BERT", "authors": [ { "first": "Rik", "middle": [], "last": "Van Noord", "suffix": "" }, { "first": "Antonio", "middle": [], "last": "Toral", "suffix": "" }, { "first": "Johan", "middle": [], "last": "Bos", "suffix": "" } ], "year": 2020, "venue": "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", "volume": "", "issue": "", "pages": "4587--4603", "other_ids": { "DOI": [ "10.18653/v1/2020.emnlp-main.371" ] }, "num": null, "urls": [], "raw_text": "Rik van Noord, Antonio Toral, and Johan Bos. 2020. Character-level representations improve DRS-based semantic parsing Even in the age of BERT. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4587-4603, Online. Association for Computa- tional Linguistics.", "links": null }, "BIBREF61": { "ref_id": "b61", "title": "Universal Dependency treebanks for low-resource Indian languages: The case of Bhojpuri", "authors": [ { "first": "Atul", "middle": [], "last": "Kr", "suffix": "" }, { "first": "Daniel", "middle": [], "last": "Ojha", "suffix": "" }, { "first": "", "middle": [], "last": "Zeman", "suffix": "" } ], "year": 2020, "venue": "Proceedings of the WILDRE5-5th Workshop on Indian Language Data: Resources and Evaluation", "volume": "", "issue": "", "pages": "33--38", "other_ids": {}, "num": null, "urls": [], "raw_text": "Atul Kr. Ojha and Daniel Zeman. 2020. Universal Dependency treebanks for low-resource Indian lan- guages: The case of Bhojpuri. In Proceedings of the WILDRE5-5th Workshop on Indian Language Data: Resources and Evaluation, pages 33-38, Marseille, France. European Language Resources Association (ELRA).", "links": null }, "BIBREF62": { "ref_id": "b62", "title": "Universal dependency for modern Japanese", "authors": [ { "first": "Mai", "middle": [], "last": "Omura", "suffix": "" }, { "first": "Yuta", "middle": [], "last": "Takahashi", "suffix": "" }, { "first": "Masayuki", "middle": [], "last": "Asahara", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the 7th Conference of Japanese Association for Digital Humanities (JADH2017)", "volume": "", "issue": "", "pages": "34--36", "other_ids": {}, "num": null, "urls": [], "raw_text": "Mai Omura, Yuta Takahashi, and Masayuki Asahara. 2017. Universal dependency for modern Japanese. In Proceedings of the 7th Conference of Japanese Association for Digital Humanities (JADH2017), pages 34-36.", "links": null }, "BIBREF63": { "ref_id": "b63", "title": "Universal Dependencies for Swedish Sign Language", "authors": [ { "first": "Carl", "middle": [], "last": "Robert\u00f6stling", "suffix": "" }, { "first": "Moa", "middle": [], "last": "B\u00f6rstell", "suffix": "" }, { "first": "Mats", "middle": [], "last": "G\u00e4rdenfors", "suffix": "" }, { "first": "", "middle": [], "last": "Wir\u00e9n", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the 21st Nordic Conference on Computational Linguistics", "volume": "", "issue": "", "pages": "303--308", "other_ids": {}, "num": null, "urls": [], "raw_text": "Robert\u00d6stling, Carl B\u00f6rstell, Moa G\u00e4rdenfors, and Mats Wir\u00e9n. 2017. Universal Dependencies for Swedish Sign Language. In Proceedings of the 21st Nordic Conference on Computational Linguis- tics, pages 303-308, Gothenburg, Sweden. Associa- tion for Computational Linguistics.", "links": null }, "BIBREF64": { "ref_id": "b64", "title": "Universal Dependencies for Norwegian", "authors": [ { "first": "Lilja", "middle": [], "last": "\u00d8vrelid", "suffix": "" }, { "first": "Petter", "middle": [], "last": "Hohle", "suffix": "" } ], "year": 2016, "venue": "Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)", "volume": "", "issue": "", "pages": "1579--1585", "other_ids": {}, "num": null, "urls": [], "raw_text": "Lilja \u00d8vrelid and Petter Hohle. 2016. Universal Depen- dencies for Norwegian. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1579-1585, Por- toro\u017e, Slovenia. European Language Resources As- sociation (ELRA).", "links": null }, "BIBREF65": { "ref_id": "b65", "title": "Per Erik Solberg, and Janne Bondi Johannessen", "authors": [ { "first": "Lilja", "middle": [], "last": "\u00d8vrelid", "suffix": "" }, { "first": "Andre", "middle": [], "last": "K\u00e5sen", "suffix": "" }, { "first": "Kristin", "middle": [], "last": "Hagen", "suffix": "" }, { "first": "Anders", "middle": [], "last": "N\u00f8klestad", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Lilja \u00d8vrelid, Andre K\u00e5sen, Kristin Hagen, Anders N\u00f8klestad, Per Erik Solberg, and Janne Bondi Jo- hannessen. 2018. The LIA treebank of spoken Nor- wegian dialects. In Proceedings of the Eleventh In- ternational Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. Euro- pean Language Resources Association (ELRA).", "links": null }, "BIBREF66": { "ref_id": "b66", "title": "Hindi syntax: Annotating dependency, lexical predicate-argument structure, and phrase structure", "authors": [ { "first": "Martha", "middle": [], "last": "Palmer", "suffix": "" }, { "first": "Rajesh", "middle": [], "last": "Bhatt", "suffix": "" }, { "first": "Bhuvana", "middle": [], "last": "Narasimhan", "suffix": "" }, { "first": "Owen", "middle": [], "last": "Rambow", "suffix": "" }, { "first": "Dipti", "middle": [ "Misra" ], "last": "Sharma", "suffix": "" }, { "first": "Fei", "middle": [], "last": "Xia", "suffix": "" } ], "year": 2009, "venue": "The 7th International Conference on Natural Language Processing", "volume": "", "issue": "", "pages": "14--17", "other_ids": {}, "num": null, "urls": [], "raw_text": "Martha Palmer, Rajesh Bhatt, Bhuvana Narasimhan, Owen Rambow, Dipti Misra Sharma, and Fei Xia. 2009. Hindi syntax: Annotating dependency, lex- ical predicate-argument structure, and phrase struc- ture. In The 7th International Conference on Natu- ral Language Processing, pages 14-17.", "links": null }, "BIBREF67": { "ref_id": "b67", "title": "The first Komi-Zyrian Universal Dependencies treebanks", "authors": [ { "first": "Niko", "middle": [], "last": "Partanen", "suffix": "" }, { "first": "Rogier", "middle": [], "last": "Blokland", "suffix": "" }, { "first": "Kyungtae", "middle": [], "last": "Lim", "suffix": "" }, { "first": "Thierry", "middle": [], "last": "Poibeau", "suffix": "" }, { "first": "Michael", "middle": [], "last": "Rie\u00dfler", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)", "volume": "", "issue": "", "pages": "126--132", "other_ids": { "DOI": [ "10.18653/v1/W18-6015" ] }, "num": null, "urls": [], "raw_text": "Niko Partanen, Rogier Blokland, KyungTae Lim, Thierry Poibeau, and Michael Rie\u00dfler. 2018. The first Komi-Zyrian Universal Dependencies tree- banks. In Proceedings of the Second Workshop on Universal Dependencies (UDW 2018), pages 126- 132, Brussels, Belgium. Association for Computa- tional Linguistics.", "links": null }, "BIBREF68": { "ref_id": "b68", "title": "PyTorch: An imperative style, high-performance deep learning library", "authors": [ { "first": "Adam", "middle": [], "last": "Paszke", "suffix": "" }, { "first": "Sam", "middle": [], "last": "Gross", "suffix": "" }, { "first": "Francisco", "middle": [], "last": "Massa", "suffix": "" }, { "first": "Adam", "middle": [], "last": "Lerer", "suffix": "" }, { "first": "James", "middle": [], "last": "Bradbury", "suffix": "" }, { "first": "Gregory", "middle": [], "last": "Chanan", "suffix": "" }, { "first": "Trevor", "middle": [], "last": "Killeen", "suffix": "" }, { "first": "Zeming", "middle": [], "last": "Lin", "suffix": "" }, { "first": "Natalia", "middle": [], "last": "Gimelshein", "suffix": "" }, { "first": "Luca", "middle": [], "last": "Antiga", "suffix": "" }, { "first": "Alban", "middle": [], "last": "Desmaison", "suffix": "" }, { "first": "Andreas", "middle": [], "last": "Kopf", "suffix": "" }, { "first": "Edward", "middle": [], "last": "Yang", "suffix": "" }, { "first": "Zachary", "middle": [], "last": "Devito", "suffix": "" }, { "first": "Martin", "middle": [], "last": "Raison", "suffix": "" }, { "first": "Alykhan", "middle": [], "last": "Tejani", "suffix": "" }, { "first": "Sasank", "middle": [], "last": "Chilamkurthy", "suffix": "" }, { "first": "Benoit", "middle": [], "last": "Steiner", "suffix": "" }, { "first": "Lu", "middle": [], "last": "Fang", "suffix": "" }, { "first": "Junjie", "middle": [], "last": "Bai", "suffix": "" }, { "first": "Soumith", "middle": [], "last": "Chintala", "suffix": "" } ], "year": 2019, "venue": "Advances in Neural Information Processing Systems", "volume": "32", "issue": "", "pages": "8026--8037", "other_ids": {}, "num": null, "urls": [], "raw_text": "Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Te- jani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An imperative style, high-performance deep learn- ing library. In Advances in Neural Information Pro- cessing Systems 32, pages 8026-8037. Vancouver, Canada.", "links": null }, "BIBREF69": { "ref_id": "b69", "title": "From Lexical Functional Grammar to Enhanced Universal Dependencies: Linguistically informed treebanks of Polish", "authors": [ { "first": "Agnieszka", "middle": [], "last": "Patejuk", "suffix": "" }, { "first": "Adam", "middle": [], "last": "Przepi\u00f3rkowski", "suffix": "" } ], "year": 2018, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Agnieszka Patejuk and Adam Przepi\u00f3rkowski. 2018. From Lexical Functional Grammar to Enhanced Universal Dependencies: Linguistically informed treebanks of Polish. Institute of Computer Science, Polish Academy of Sciences, Warsaw.", "links": null }, "BIBREF70": { "ref_id": "b70", "title": "Deep contextualized word representations", "authors": [ { "first": "Matthew", "middle": [], "last": "Peters", "suffix": "" }, { "first": "Mark", "middle": [], "last": "Neumann", "suffix": "" }, { "first": "Mohit", "middle": [], "last": "Iyyer", "suffix": "" }, { "first": "Matt", "middle": [], "last": "Gardner", "suffix": "" }, { "first": "Christopher", "middle": [], "last": "Clark", "suffix": "" }, { "first": "Kenton", "middle": [], "last": "Lee", "suffix": "" }, { "first": "Luke", "middle": [], "last": "Zettlemoyer", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", "volume": "1", "issue": "", "pages": "2227--2237", "other_ids": { "DOI": [ "10.18653/v1/N18-1202" ] }, "num": null, "urls": [], "raw_text": "Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word rep- resentations. In Proceedings of the 2018 Confer- ence of the North American Chapter of the Associ- ation for Computational Linguistics: Human Lan- guage Technologies, Volume 1 (Long Papers), pages 2227-2237, New Orleans, Louisiana. Association for Computational Linguistics.", "links": null }, "BIBREF72": { "ref_id": "b72", "title": "Online. Association for Computational Linguistics", "authors": [], "year": null, "venue": "", "volume": "", "issue": "", "pages": "109--117", "other_ids": {}, "num": null, "urls": [], "raw_text": "System Demonstrations, pages 109-117, Online. As- sociation for Computational Linguistics.", "links": null }, "BIBREF73": { "ref_id": "b73", "title": "Universal Dependencies for Finnish", "authors": [ { "first": "Sampo", "middle": [], "last": "Pyysalo", "suffix": "" }, { "first": "Jenna", "middle": [], "last": "Kanerva", "suffix": "" }, { "first": "Anna", "middle": [], "last": "Missil\u00e4", "suffix": "" }, { "first": "Veronika", "middle": [], "last": "Laippala", "suffix": "" }, { "first": "Filip", "middle": [], "last": "Ginter", "suffix": "" } ], "year": 2015, "venue": "Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015)", "volume": "", "issue": "", "pages": "163--172", "other_ids": {}, "num": null, "urls": [], "raw_text": "Sampo Pyysalo, Jenna Kanerva, Anna Missil\u00e4, Veronika Laippala, and Filip Ginter. 2015. Univer- sal Dependencies for Finnish. In Proceedings of the 20th Nordic Conference of Computational Linguis- tics (NODALIDA 2015), pages 163-172, Vilnius, Lithuania. Link\u00f6ping University Electronic Press, Sweden.", "links": null }, "BIBREF75": { "ref_id": "b75", "title": "Universal Dependencies for Portuguese", "authors": [ { "first": "Alexandre", "middle": [], "last": "Rademaker", "suffix": "" }, { "first": "Fabricio", "middle": [], "last": "Chalub", "suffix": "" }, { "first": "Livy", "middle": [], "last": "Real", "suffix": "" }, { "first": "Cl\u00e1udia", "middle": [], "last": "Freitas", "suffix": "" }, { "first": "Eckhard", "middle": [], "last": "Bick", "suffix": "" }, { "first": "Valeria", "middle": [], "last": "De Paiva", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the Fourth International Conference on Dependency Linguistics", "volume": "", "issue": "", "pages": "197--206", "other_ids": {}, "num": null, "urls": [], "raw_text": "Alexandre Rademaker, Fabricio Chalub, Livy Real, Cl\u00e1udia Freitas, Eckhard Bick, and Valeria de Paiva. 2017. Universal Dependencies for Portuguese. In Proceedings of the Fourth International Confer- ence on Dependency Linguistics (Depling 2017), pages 197-206, Pisa,Italy. Link\u00f6ping University Electronic Press.", "links": null }, "BIBREF76": { "ref_id": "b76", "title": "Language models are unsupervised multitask learners", "authors": [ { "first": "Alec", "middle": [], "last": "Radford", "suffix": "" }, { "first": "Jeffrey", "middle": [], "last": "Wu", "suffix": "" }, { "first": "Rewon", "middle": [], "last": "Child", "suffix": "" }, { "first": "David", "middle": [], "last": "Luan", "suffix": "" }, { "first": "Dario", "middle": [], "last": "Amodei", "suffix": "" }, { "first": "Ilya", "middle": [], "last": "Sutskever", "suffix": "" } ], "year": 2019, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog.", "links": null }, "BIBREF77": { "ref_id": "b77", "title": "Know what you don't know: Unanswerable questions for SQuAD", "authors": [ { "first": "Pranav", "middle": [], "last": "Rajpurkar", "suffix": "" }, { "first": "Robin", "middle": [], "last": "Jia", "suffix": "" }, { "first": "Percy", "middle": [], "last": "Liang", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics", "volume": "2", "issue": "", "pages": "784--789", "other_ids": { "DOI": [ "10.18653/v1/P18-2124" ] }, "num": null, "urls": [], "raw_text": "Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. Know what you don't know: Unanswerable ques- tions for SQuAD. In Proceedings of the 56th An- nual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 784- 789, Melbourne, Australia. Association for Compu- tational Linguistics.", "links": null }, "BIBREF78": { "ref_id": "b78", "title": "A Telugu treebank based on a grammar book", "authors": [ { "first": "Taraka", "middle": [], "last": "Rama", "suffix": "" }, { "first": "Sowmya", "middle": [], "last": "Vajjala", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories", "volume": "", "issue": "", "pages": "119--128", "other_ids": {}, "num": null, "urls": [], "raw_text": "Taraka Rama and Sowmya Vajjala. 2017. A Telugu treebank based on a grammar book. In Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories, pages 119-128, Prague, Czech Republic.", "links": null }, "BIBREF79": { "ref_id": "b79", "title": "Prague dependency style treebank for Tamil", "authors": [ { "first": "Loganathan", "middle": [], "last": "Ramasamy", "suffix": "" }, { "first": "", "middle": [], "last": "Zden\u011bk\u017eabokrtsk\u00fd", "suffix": "" } ], "year": 2012, "venue": "Proceedings of Eighth International Conference on Language Resources and Evaluation (LREC 2012)", "volume": "", "issue": "", "pages": "1888--1894", "other_ids": {}, "num": null, "urls": [], "raw_text": "Loganathan Ramasamy and Zden\u011bk\u017dabokrtsk\u00fd. 2012. Prague dependency style treebank for Tamil. In Proceedings of Eighth International Conference on Language Resources and Evaluation (LREC 2012), pages 1888-1894,\u0130stanbul, Turkey.", "links": null }, "BIBREF80": { "ref_id": "b80", "title": "A Universal Dependencies treebank for Marathi", "authors": [ { "first": "", "middle": [], "last": "Vinit Ravishankar", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories", "volume": "", "issue": "", "pages": "190--200", "other_ids": {}, "num": null, "urls": [], "raw_text": "Vinit Ravishankar. 2017. A Universal Dependencies treebank for Marathi. In Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories, pages 190-200, Prague, Czech Republic.", "links": null }, "BIBREF81": { "ref_id": "b81", "title": "tweeDe -a Universal Dependencies treebank for German tweets", "authors": [ { "first": "Ines", "middle": [], "last": "Rehbein", "suffix": "" }, { "first": "Josef", "middle": [], "last": "Ruppenhofer", "suffix": "" }, { "first": "Bich-Ngoc", "middle": [], "last": "Do", "suffix": "" } ], "year": 2019, "venue": "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)", "volume": "", "issue": "", "pages": "100--108", "other_ids": { "DOI": [ "10.18653/v1/W19-7811" ] }, "num": null, "urls": [], "raw_text": "Ines Rehbein, Josef Ruppenhofer, and Bich-Ngoc Do. 2019. tweeDe -a Universal Dependencies treebank for German tweets. In Proceedings of the 18th Inter- national Workshop on Treebanks and Linguistic The- ories (TLT, SyntaxFest 2019), pages 100-108, Paris, France. Association for Computational Linguistics.", "links": null }, "BIBREF82": { "ref_id": "b82", "title": "The Icelandic parsed historical corpus (IcePaHC)", "authors": [ { "first": "Eir\u00edkur", "middle": [], "last": "R\u00f6gnvaldsson", "suffix": "" }, { "first": "Anton", "middle": [ "Karl" ], "last": "Ingason", "suffix": "" }, { "first": "Einar", "middle": [], "last": "Freyr Sigurosson", "suffix": "" }, { "first": "Joel", "middle": [], "last": "Wallenberg", "suffix": "" } ], "year": 2012, "venue": "Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)", "volume": "", "issue": "", "pages": "1977--1984", "other_ids": {}, "num": null, "urls": [], "raw_text": "Eir\u00edkur R\u00f6gnvaldsson, Anton Karl Ingason, Einar Freyr Sigurosson, and Joel Wallenberg. 2012. The Ice- landic parsed historical corpus (IcePaHC). In Pro- ceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 1977-1984, Istanbul, Turkey. European Lan- guage Resources Association (ELRA).", "links": null }, "BIBREF83": { "ref_id": "b83", "title": "An overview of multi-task learning in", "authors": [ { "first": "Sebastian", "middle": [], "last": "Ruder", "suffix": "" } ], "year": 2017, "venue": "deep neural networks", "volume": "", "issue": "", "pages": "", "other_ids": { "arXiv": [ "arXiv:1706.05098" ] }, "num": null, "urls": [], "raw_text": "Sebastian Ruder. 2017. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098.", "links": null }, "BIBREF84": { "ref_id": "b84", "title": "Strong baselines for neural semi-supervised learning under domain shift", "authors": [ { "first": "Sebastian", "middle": [], "last": "Ruder", "suffix": "" }, { "first": "Barbara", "middle": [], "last": "Plank", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics", "volume": "1", "issue": "", "pages": "1044--1054", "other_ids": { "DOI": [ "10.18653/v1/P18-1096" ] }, "num": null, "urls": [], "raw_text": "Sebastian Ruder and Barbara Plank. 2018. Strong base- lines for neural semi-supervised learning under do- main shift. In Proceedings of the 56th Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1044-1054, Mel- bourne, Australia. Association for Computational Linguistics.", "links": null }, "BIBREF85": { "ref_id": "b85", "title": "Survey of Uralic Universal Dependencies development", "authors": [ { "first": "Jack", "middle": [], "last": "Rueter", "suffix": "" }, { "first": "Niko", "middle": [], "last": "Partanen", "suffix": "" } ], "year": 2019, "venue": "Workshop on Universal Dependencies, page 78. The Association for Computational Linguistics", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Jack Rueter and Niko Partanen. 2019. Survey of Uralic Universal Dependencies development. In Workshop on Universal Dependencies, page 78. The Associa- tion for Computational Linguistics.", "links": null }, "BIBREF86": { "ref_id": "b86", "title": "On the questions in developing computational infrastructure for Komi-permyak", "authors": [ { "first": "Jack", "middle": [], "last": "Rueter", "suffix": "" }, { "first": "Niko", "middle": [], "last": "Partanen", "suffix": "" }, { "first": "Larisa", "middle": [], "last": "Ponomareva", "suffix": "" } ], "year": 2020, "venue": "Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages", "volume": "", "issue": "", "pages": "15--25", "other_ids": {}, "num": null, "urls": [], "raw_text": "Jack Rueter, Niko Partanen, and Larisa Ponomareva. 2020. On the questions in developing computational infrastructure for Komi-permyak. In Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages, pages 15-25, Wien, Austria. Association for Computational Linguistics.", "links": null }, "BIBREF87": { "ref_id": "b87", "title": "Towards an opensource universal-dependency treebank for Erzya", "authors": [ { "first": "Jack", "middle": [], "last": "Rueter", "suffix": "" }, { "first": "Francis", "middle": [], "last": "Tyers", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages", "volume": "", "issue": "", "pages": "106--118", "other_ids": {}, "num": null, "urls": [], "raw_text": "Jack Rueter and Francis Tyers. 2018. Towards an open- source universal-dependency treebank for Erzya. In Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages, pages 106-118.", "links": null }, "BIBREF88": { "ref_id": "b88", "title": "The Uralic Languages. Routledge", "authors": [ { "first": "Jack", "middle": [], "last": "Michael Rueter", "suffix": "" } ], "year": 2018, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Jack Michael Rueter. 2018. Mordva. In The Uralic Languages. Routledge.", "links": null }, "BIBREF89": { "ref_id": "b89", "title": "Amirsaeid Moloodi, and Alireza Nourian. 2020. The Persian dependency treebank made universal", "authors": [ { "first": "Mohammad", "middle": [], "last": "Sadegh Rasooli", "suffix": "" }, { "first": "Pegah", "middle": [], "last": "Safari", "suffix": "" } ], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Mohammad Sadegh Rasooli, Pegah Safari, Amirsaeid Moloodi, and Alireza Nourian. 2020. The Per- sian dependency treebank made universal. arXiv e- prints, pages arXiv-2009.", "links": null }, "BIBREF90": { "ref_id": "b90", "title": "UD German-LIT", "authors": [ { "first": "Alessio", "middle": [], "last": "Salomoni", "suffix": "" } ], "year": 2019, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Alessio Salomoni. 2019. UD German-LIT. https: //github.com/UniversalDependencies/UD_ German-LIT.", "links": null }, "BIBREF91": { "ref_id": "b91", "title": "Universal Dependencies for Serbian in comparison with Croatian and other Slavic languages", "authors": [ { "first": "Tanja", "middle": [], "last": "Samard\u017ei\u0107", "suffix": "" }, { "first": "Mirjana", "middle": [], "last": "Starovi\u0107", "suffix": "" }, { "first": "\u017deljko", "middle": [], "last": "Agi\u0107", "suffix": "" }, { "first": "Nikola", "middle": [], "last": "Ljube\u0161i\u0107", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing", "volume": "", "issue": "", "pages": "39--44", "other_ids": { "DOI": [ "10.18653/v1/W17-1407" ] }, "num": null, "urls": [], "raw_text": "Tanja Samard\u017ei\u0107, Mirjana Starovi\u0107,\u017deljko Agi\u0107, and Nikola Ljube\u0161i\u0107. 2017. Universal Dependencies for Serbian in comparison with Croatian and other Slavic languages. In Proceedings of the 6th Work- shop on Balto-Slavic Natural Language Processing, pages 39-44, Valencia, Spain. Association for Com- putational Linguistics.", "links": null }, "BIBREF92": { "ref_id": "b92", "title": "Towards a Universal Stanford Dependencies parallel treebank", "authors": [ { "first": "Manuela", "middle": [], "last": "Sanguinetti", "suffix": "" }, { "first": "Cristina", "middle": [], "last": "Bosco", "suffix": "" } ], "year": 2014, "venue": "Proceedings of the 13th Workshop on Treebanks and Linguistic Theories (TLT-13)", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Manuela Sanguinetti and Cristina Bosco. 2014. To- wards a Universal Stanford Dependencies paral- lel treebank. In Proceedings of the 13th Work- shop on Treebanks and Linguistic Theories (TLT-13). Springer.", "links": null }, "BIBREF93": { "ref_id": "b93", "title": "PoSTWITA-UD: an Italian Twitter treebank in Universal Dependencies", "authors": [ { "first": "Manuela", "middle": [], "last": "Sanguinetti", "suffix": "" }, { "first": "Cristina", "middle": [], "last": "Bosco", "suffix": "" }, { "first": "Alberto", "middle": [], "last": "Lavelli", "suffix": "" }, { "first": "Alessandro", "middle": [], "last": "Mazzei", "suffix": "" }, { "first": "Oronzo", "middle": [], "last": "Antonelli", "suffix": "" }, { "first": "Fabio", "middle": [], "last": "Tamburini", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Manuela Sanguinetti, Cristina Bosco, Alberto Lavelli, Alessandro Mazzei, Oronzo Antonelli, and Fabio Tamburini. 2018. PoSTWITA-UD: an Italian Twit- ter treebank in Universal Dependencies. In Proceed- ings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources As- sociation (ELRA).", "links": null }, "BIBREF94": { "ref_id": "b94", "title": "A hierarchical multi-task approach for learning embeddings from semantic tasks", "authors": [ { "first": "Victor", "middle": [], "last": "Sanh", "suffix": "" }, { "first": "Thomas", "middle": [], "last": "Wolf", "suffix": "" }, { "first": "Sebastian", "middle": [], "last": "Ruder", "suffix": "" } ], "year": 2019, "venue": "Proceedings of the AAAI Conference on Artificial Intelligence", "volume": "33", "issue": "", "pages": "6949--6956", "other_ids": {}, "num": null, "urls": [], "raw_text": "Victor Sanh, Thomas Wolf, and Sebastian Ruder. 2019. A hierarchical multi-task approach for learning em- beddings from semantic tasks. In Proceedings of the AAAI Conference on Artificial Intelligence, vol- ume 33, pages 6949-6956, Honolulu, Hawaii, USA.", "links": null }, "BIBREF95": { "ref_id": "b95", "title": "ThamizhiUDp: A dependency parser for Tamil", "authors": [ { "first": "Kengatharaiyer", "middle": [], "last": "Sarveswaran", "suffix": "" }, { "first": "Gihan", "middle": [], "last": "Dias", "suffix": "" } ], "year": 2020, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": { "arXiv": [ "arXiv:2012.13436" ] }, "num": null, "urls": [], "raw_text": "Kengatharaiyer Sarveswaran and Gihan Dias. 2020. ThamizhiUDp: A dependency parser for Tamil. arXiv preprint arXiv:2012.13436.", "links": null }, "BIBREF96": { "ref_id": "b96", "title": "Universal Dependencies for Manx Gaelic", "authors": [ { "first": "Kevin", "middle": [], "last": "Scannell", "suffix": "" } ], "year": 2020, "venue": "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)", "volume": "", "issue": "", "pages": "152--157", "other_ids": {}, "num": null, "urls": [], "raw_text": "Kevin Scannell. 2020. Universal Dependencies for Manx Gaelic. In Proceedings of the Fourth Work- shop on Universal Dependencies (UDW 2020), pages 152-157, Barcelona, Spain (Online). Associ- ation for Computational Linguistics.", "links": null }, "BIBREF97": { "ref_id": "b97", "title": "Hard time parsing questions: Building a QuestionBank for French", "authors": [ { "first": "Djam\u00e9", "middle": [], "last": "Seddah", "suffix": "" }, { "first": "Marie", "middle": [], "last": "Candito", "suffix": "" } ], "year": 2016, "venue": "Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)", "volume": "", "issue": "", "pages": "2366--2370", "other_ids": {}, "num": null, "urls": [], "raw_text": "Djam\u00e9 Seddah and Marie Candito. 2016. Hard time parsing questions: Building a QuestionBank for French. In Proceedings of the Tenth Inter- national Conference on Language Resources and Evaluation (LREC'16), pages 2366-2370, Portoro\u017e, Slovenia. European Language Resources Associa- tion (ELRA).", "links": null }, "BIBREF98": { "ref_id": "b98", "title": "Universal Dependencies for Persian", "authors": [ { "first": "Mojgan", "middle": [], "last": "Seraji", "suffix": "" }, { "first": "Filip", "middle": [], "last": "Ginter", "suffix": "" }, { "first": "Joakim", "middle": [], "last": "Nivre", "suffix": "" } ], "year": 2016, "venue": "Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)", "volume": "", "issue": "", "pages": "2361--2365", "other_ids": {}, "num": null, "urls": [], "raw_text": "Mojgan Seraji, Filip Ginter, and Joakim Nivre. 2016. Universal Dependencies for Persian. In Proceed- ings of the Tenth International Conference on Lan- guage Resources and Evaluation (LREC'16), pages 2361-2365, Portoro\u017e, Slovenia. European Language Resources Association (ELRA).", "links": null }, "BIBREF99": { "ref_id": "b99", "title": "Universal Dependencies for Amharic", "authors": [ { "first": "Ephrem", "middle": [], "last": "Binyam", "suffix": "" }, { "first": "Yusuke", "middle": [], "last": "Seyoum", "suffix": "" }, { "first": "Baye Yimam", "middle": [], "last": "Miyao", "suffix": "" }, { "first": "", "middle": [], "last": "Mekonnen", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Binyam Ephrem Seyoum, Yusuke Miyao, and Baye Yi- mam Mekonnen. 2018. Universal Dependencies for Amharic. In Proceedings of the Eleventh Interna- tional Conference on Language Resources and Eval- uation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).", "links": null }, "BIBREF100": { "ref_id": "b100", "title": "To the methodology of corpus construction for machine learning:\"Taiga\" syntax tree corpus and parser", "authors": [ { "first": "Tatiana", "middle": [], "last": "Shavrina", "suffix": "" }, { "first": "Olga", "middle": [], "last": "Shapovalova", "suffix": "" } ], "year": 2017, "venue": "Proceedings of \"CORPORA-2017\" International Conference", "volume": "", "issue": "", "pages": "78--84", "other_ids": {}, "num": null, "urls": [], "raw_text": "Tatiana Shavrina and Olga Shapovalova. 2017. To the methodology of corpus construction for ma- chine learning:\"Taiga\" syntax tree corpus and parser. In Proceedings of \"CORPORA-2017\" International Conference, pages 78-84.", "links": null }, "BIBREF102": { "ref_id": "b102", "title": "UD Warlpiri-UFAL", "authors": [ { "first": "Timothy", "middle": [], "last": "Shopen", "suffix": "" } ], "year": 2018, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Timothy Shopen. 2018. UD Warlpiri-UFAL. https: //github.com/UniversalDependencies/UD_ Lithuanian-HSE.", "links": null }, "BIBREF103": { "ref_id": "b103", "title": "A gold standard dependency corpus for English", "authors": [ { "first": "Natalia", "middle": [], "last": "Silveira", "suffix": "" }, { "first": "Timothy", "middle": [], "last": "Dozat", "suffix": "" }, { "first": "Marie-Catherine", "middle": [], "last": "De Marneffe", "suffix": "" }, { "first": "Samuel", "middle": [], "last": "Bowman", "suffix": "" }, { "first": "Miriam", "middle": [], "last": "Connor", "suffix": "" }, { "first": "John", "middle": [], "last": "Bauer", "suffix": "" }, { "first": "Chris", "middle": [], "last": "Manning", "suffix": "" } ], "year": 2014, "venue": "Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)", "volume": "", "issue": "", "pages": "2897--2904", "other_ids": {}, "num": null, "urls": [], "raw_text": "Natalia Silveira, Timothy Dozat, Marie-Catherine de Marneffe, Samuel Bowman, Miriam Connor, John Bauer, and Chris Manning. 2014. A gold stan- dard dependency corpus for English. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 2897- 2904, Reykjavik, Iceland. European Language Re- sources Association (ELRA).", "links": null }, "BIBREF104": { "ref_id": "b104", "title": "Design and implementation of the Bulgarian HPSG-based treebank", "authors": [ { "first": "Kiril", "middle": [], "last": "Simov", "suffix": "" }, { "first": "Petya", "middle": [], "last": "Osenova", "suffix": "" }, { "first": "Alexander", "middle": [], "last": "Simov", "suffix": "" }, { "first": "Milen", "middle": [], "last": "Kouylekov", "suffix": "" } ], "year": 2005, "venue": "Journal of Research on Language and Computation. Special Issue", "volume": "", "issue": "", "pages": "495--522", "other_ids": {}, "num": null, "urls": [], "raw_text": "Kiril Simov, Petya Osenova, Alexander Simov, and Milen Kouylekov. 2005. Design and implementa- tion of the Bulgarian HPSG-based treebank. Jour- nal of Research on Language and Computation. Spe- cial Issue, pages 495-522.", "links": null }, "BIBREF105": { "ref_id": "b105", "title": "Recursive deep models for semantic compositionality over a sentiment treebank", "authors": [ { "first": "Richard", "middle": [], "last": "Socher", "suffix": "" }, { "first": "Alex", "middle": [], "last": "Perelygin", "suffix": "" }, { "first": "Jean", "middle": [], "last": "Wu", "suffix": "" }, { "first": "Jason", "middle": [], "last": "Chuang", "suffix": "" }, { "first": "Christopher", "middle": [ "D" ], "last": "Manning", "suffix": "" }, { "first": "Andrew", "middle": [], "last": "Ng", "suffix": "" }, { "first": "Christopher", "middle": [], "last": "Potts", "suffix": "" } ], "year": 2013, "venue": "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", "volume": "", "issue": "", "pages": "1631--1642", "other_ids": {}, "num": null, "urls": [], "raw_text": "Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment tree- bank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1631-1642, Seattle, Washington, USA. Asso- ciation for Computational Linguistics.", "links": null }, "BIBREF106": { "ref_id": "b106", "title": "Deep multitask learning with low level tasks supervised at lower layers", "authors": [ { "first": "Anders", "middle": [], "last": "S\u00f8gaard", "suffix": "" }, { "first": "Yoav", "middle": [], "last": "Goldberg", "suffix": "" } ], "year": 2016, "venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics", "volume": "2", "issue": "", "pages": "231--235", "other_ids": { "DOI": [ "10.18653/v1/P16-2038" ] }, "num": null, "urls": [], "raw_text": "Anders S\u00f8gaard and Yoav Goldberg. 2016. Deep multi- task learning with low level tasks supervised at lower layers. In Proceedings of the 54th Annual Meet- ing of the Association for Computational Linguistics (Volume 2: Short Papers), pages 231-235, Berlin, Germany. Association for Computational Linguis- tics.", "links": null }, "BIBREF107": { "ref_id": "b107", "title": "Syntactic annotation of medieval texts", "authors": [ { "first": "Achim", "middle": [], "last": "Stein", "suffix": "" }, { "first": "Sophie", "middle": [], "last": "Pr\u00e9vost", "suffix": "" } ], "year": 2013, "venue": "New methods in historical corpora", "volume": "3", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Achim Stein and Sophie Pr\u00e9vost. 2013. Syntactic anno- tation of medieval texts. New methods in historical corpora, 3:275.", "links": null }, "BIBREF108": { "ref_id": "b108", "title": "UDPipe 2.0 prototype at CoNLL 2018 UD shared task", "authors": [ { "first": "Milan", "middle": [], "last": "Straka", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies", "volume": "", "issue": "", "pages": "197--207", "other_ids": { "DOI": [ "10.18653/v1/K18-2020" ] }, "num": null, "urls": [], "raw_text": "Milan Straka. 2018. UDPipe 2.0 prototype at CoNLL 2018 UD shared task. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 197-207, Brussels, Belgium. Association for Computational Linguistics.", "links": null }, "BIBREF109": { "ref_id": "b109", "title": "Parser training with heterogeneous treebanks", "authors": [ { "first": "Sara", "middle": [], "last": "Stymne", "suffix": "" }, { "first": "Aaron", "middle": [], "last": "Miryam De Lhoneux", "suffix": "" }, { "first": "Joakim", "middle": [], "last": "Smith", "suffix": "" }, { "first": "", "middle": [], "last": "Nivre", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics", "volume": "2", "issue": "", "pages": "619--625", "other_ids": { "DOI": [ "10.18653/v1/P18-2098" ] }, "num": null, "urls": [], "raw_text": "Sara Stymne, Miryam de Lhoneux, Aaron Smith, and Joakim Nivre. 2018. Parser training with hetero- geneous treebanks. In Proceedings of the 56th An- nual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 619- 625, Melbourne, Australia. Association for Compu- tational Linguistics.", "links": null }, "BIBREF110": { "ref_id": "b110", "title": "Universal Dependencies for Turkish", "authors": [ { "first": "Umut", "middle": [], "last": "Sulubacak", "suffix": "" }, { "first": "Memduh", "middle": [], "last": "Gokirmak", "suffix": "" }, { "first": "Francis", "middle": [], "last": "Tyers", "suffix": "" }, { "first": "Joakim", "middle": [], "last": "Agr\u0131 \u00c7\u00f6ltekin", "suffix": "" }, { "first": "G\u00fcl\u015fen", "middle": [], "last": "Nivre", "suffix": "" }, { "first": "", "middle": [], "last": "Eryigit", "suffix": "" } ], "year": 2016, "venue": "Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers", "volume": "", "issue": "", "pages": "3444--3454", "other_ids": {}, "num": null, "urls": [], "raw_text": "Umut Sulubacak, Memduh Gokirmak, Francis Tyers, \u00c7 agr\u0131 \u00c7\u00f6ltekin, Joakim Nivre, and G\u00fcl\u015fen Eryigit. 2016. Universal Dependencies for Turkish. In Pro- ceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Techni- cal Papers, pages 3444-3454, Osaka, Japan. The COLING 2016 Organizing Committee.", "links": null }, "BIBREF111": { "ref_id": "b111", "title": "Sequence to sequence learning with neural networks", "authors": [ { "first": "Ilya", "middle": [], "last": "Sutskever", "suffix": "" }, { "first": "Oriol", "middle": [], "last": "Vinyals", "suffix": "" }, { "first": "Quoc V", "middle": [], "last": "Le", "suffix": "" } ], "year": 2014, "venue": "Advances in Neural Information Processing Systems", "volume": "27", "issue": "", "pages": "3104--3112", "other_ids": {}, "num": null, "urls": [], "raw_text": "Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural net- works. In Advances in Neural Information Process- ing Systems, volume 27, pages 3104-3112, Mon- treal, Canada.", "links": null }, "BIBREF112": { "ref_id": "b112", "title": "Universal Dependencies for Mby\u00e1 Guaran\u00ed", "authors": [ { "first": "Guillaume", "middle": [], "last": "Thomas", "suffix": "" } ], "year": 2019, "venue": "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)", "volume": "", "issue": "", "pages": "70--77", "other_ids": { "DOI": [ "10.18653/v1/W19-8008" ] }, "num": null, "urls": [], "raw_text": "Guillaume Thomas. 2019. Universal Dependencies for Mby\u00e1 Guaran\u00ed. In Proceedings of the Third Work- shop on Universal Dependencies (UDW, SyntaxFest 2019), pages 70-77, Paris, France. Association for Computational Linguistics.", "links": null }, "BIBREF113": { "ref_id": "b113", "title": "Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition", "authors": [ { "first": "Erik", "middle": [ "F" ], "last": "Tjong", "suffix": "" }, { "first": "Kim", "middle": [], "last": "Sang", "suffix": "" }, { "first": "Fien", "middle": [], "last": "De Meulder", "suffix": "" } ], "year": 2003, "venue": "Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003", "volume": "", "issue": "", "pages": "142--147", "other_ids": {}, "num": null, "urls": [], "raw_text": "Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the Seventh Conference on Natu- ral Language Learning at HLT-NAACL 2003, pages 142-147.", "links": null }, "BIBREF114": { "ref_id": "b114", "title": "Universal Dependencies for Albanian", "authors": [ { "first": "Marsida", "middle": [], "last": "Toska", "suffix": "" }, { "first": "Joakim", "middle": [], "last": "Nivre", "suffix": "" }, { "first": "Daniel", "middle": [], "last": "Zeman", "suffix": "" } ], "year": 2020, "venue": "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)", "volume": "", "issue": "", "pages": "178--188", "other_ids": {}, "num": null, "urls": [], "raw_text": "Marsida Toska, Joakim Nivre, and Daniel Zeman. 2020. Universal Dependencies for Albanian. In Proceed- ings of the Fourth Workshop on Universal Depen- dencies (UDW 2020), pages 178-188, Barcelona, Spain (Online). Association for Computational Lin- guistics.", "links": null }, "BIBREF115": { "ref_id": "b115", "title": "Tunga G\u00fcng\u00f6r, and Arzu-can\u00d6zg\u00fcr. 2020. Resources for Turkish dependency parsing: Introducing the BOUN treebank and the BoAT annotation tool", "authors": [ { "first": "Utku", "middle": [], "last": "T\u00fcrk", "suffix": "" }, { "first": "Furkan", "middle": [], "last": "Atmaca", "suffix": "" }, { "first": "G\u00f6zde", "middle": [], "last": "Bet\u00fcl\u00f6zate\u015f", "suffix": "" }, { "first": "", "middle": [], "last": "Berk", "suffix": "" }, { "first": "Abdullatif", "middle": [], "last": "Seyyit Talha Bedir", "suffix": "" }, { "first": "Balkiz\u00f6zt\u00fcrk", "middle": [], "last": "K\u00f6ksal", "suffix": "" }, { "first": "", "middle": [], "last": "Ba\u015faran", "suffix": "" } ], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Utku T\u00fcrk, Furkan Atmaca, \u015e aziye Bet\u00fcl\u00d6zate\u015f, G\u00f6zde Berk, Seyyit Talha Bedir, Abdullatif K\u00f6ksal, Balkiz\u00d6zt\u00fcrk Ba\u015faran, Tunga G\u00fcng\u00f6r, and Arzu- can\u00d6zg\u00fcr. 2020. Resources for Turkish dependency parsing: Introducing the BOUN treebank and the BoAT annotation tool.", "links": null }, "BIBREF116": { "ref_id": "b116", "title": "Dependency annotation of noun incorporation in polysynthetic languages", "authors": [ { "first": "Francis", "middle": [], "last": "Tyers", "suffix": "" }, { "first": "Karina", "middle": [], "last": "Mishchenkova", "suffix": "" } ], "year": 2020, "venue": "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)", "volume": "", "issue": "", "pages": "195--204", "other_ids": {}, "num": null, "urls": [], "raw_text": "Francis Tyers and Karina Mishchenkova. 2020. Depen- dency annotation of noun incorporation in polysyn- thetic languages. In Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020), pages 195-204, Barcelona, Spain (Online). Associa- tion for Computational Linguistics.", "links": null }, "BIBREF117": { "ref_id": "b117", "title": "Multi-source synthetic treebank creation for improved cross-lingual dependency parsing", "authors": [ { "first": "Francis", "middle": [], "last": "Tyers", "suffix": "" }, { "first": "Mariya", "middle": [], "last": "Sheyanova", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)", "volume": "", "issue": "", "pages": "144--150", "other_ids": { "DOI": [ "10.18653/v1/W18-6017" ] }, "num": null, "urls": [], "raw_text": "Francis Tyers, Mariya Sheyanova, Aleksandra Mar- tynova, Pavel Stepachev, and Konstantin Vinogorod- skiy. 2018. Multi-source synthetic treebank creation for improved cross-lingual dependency parsing. In Proceedings of the Second Workshop on Universal Dependencies (UDW 2018), pages 144-150, Brus- sels, Belgium. Association for Computational Lin- guistics.", "links": null }, "BIBREF118": { "ref_id": "b118", "title": "A prototype dependency treebank for Breton", "authors": [ { "first": "M", "middle": [], "last": "Francis", "suffix": "" }, { "first": "Vinit", "middle": [], "last": "Tyers", "suffix": "" }, { "first": "", "middle": [], "last": "Ravishankar", "suffix": "" } ], "year": 2018, "venue": "Articles longs, articles courts de TALN", "volume": "1", "issue": "", "pages": "197--204", "other_ids": {}, "num": null, "urls": [], "raw_text": "Francis M Tyers and Vinit Ravishankar. 2018. A proto- type dependency treebank for Breton. In Actes de la Conf\u00e9rence TALN. Volume 1 -Articles longs, articles courts de TALN, pages 197-204, Rennes, France. ATALA.", "links": null }, "BIBREF119": { "ref_id": "b119", "title": "Annotation schemes in North S\u00e1mi dependency parsing", "authors": [ { "first": "M", "middle": [], "last": "Francis", "suffix": "" }, { "first": "Mariya", "middle": [], "last": "Tyers", "suffix": "" }, { "first": "", "middle": [], "last": "Sheyanova", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages", "volume": "", "issue": "", "pages": "66--75", "other_ids": { "DOI": [ "10.18653/v1/W17-0607" ] }, "num": null, "urls": [], "raw_text": "Francis M. Tyers and Mariya Sheyanova. 2017. Anno- tation schemes in North S\u00e1mi dependency parsing. In Proceedings of the Third Workshop on Computa- tional Linguistics for Uralic Languages, pages 66- 75, St. Petersburg, Russia. Association for Computa- tional Linguistics.", "links": null }, "BIBREF120": { "ref_id": "b120", "title": "Zolt\u00e1n Alexin, and J\u00e1nos Csirik", "authors": [ { "first": "Veronika", "middle": [], "last": "Vincze", "suffix": "" }, { "first": "D\u00f3ra", "middle": [], "last": "Szauter", "suffix": "" }, { "first": "Attila", "middle": [], "last": "Alm\u00e1si", "suffix": "" }, { "first": "Gy\u00f6rgy", "middle": [], "last": "M\u00f3ra", "suffix": "" } ], "year": 2010, "venue": "Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Veronika Vincze, D\u00f3ra Szauter, Attila Alm\u00e1si, Gy\u00f6rgy M\u00f3ra, Zolt\u00e1n Alexin, and J\u00e1nos Csirik. 2010. Hungarian dependency treebank. In Proceedings of the Seventh International Conference on Lan- guage Resources and Evaluation (LREC'10), Val- letta, Malta. European Language Resources Associ- ation (ELRA).", "links": null }, "BIBREF122": { "ref_id": "b122", "title": "Treebank embedding vectors for out-ofdomain dependency parsing", "authors": [ { "first": "Joachim", "middle": [], "last": "Wagner", "suffix": "" }, { "first": "James", "middle": [], "last": "Barry", "suffix": "" }, { "first": "Jennifer", "middle": [], "last": "Foster", "suffix": "" } ], "year": 2020, "venue": "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", "volume": "", "issue": "", "pages": "8812--8818", "other_ids": { "DOI": [ "10.18653/v1/2020.acl-main.778" ] }, "num": null, "urls": [], "raw_text": "Joachim Wagner, James Barry, and Jennifer Foster. 2020. Treebank embedding vectors for out-of- domain dependency parsing. In Proceedings of the 58th Annual Meeting of the Association for Compu- tational Linguistics, pages 8812-8818, Online. As- sociation for Computational Linguistics.", "links": null }, "BIBREF123": { "ref_id": "b123", "title": "GLUE: A multi-task benchmark and analysis platform for natural language understanding", "authors": [ { "first": "Alex", "middle": [], "last": "Wang", "suffix": "" }, { "first": "Amanpreet", "middle": [], "last": "Singh", "suffix": "" }, { "first": "Julian", "middle": [], "last": "Michael", "suffix": "" }, { "first": "Felix", "middle": [], "last": "Hill", "suffix": "" }, { "first": "Omer", "middle": [], "last": "Levy", "suffix": "" }, { "first": "Samuel", "middle": [], "last": "Bowman", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the 2018 EMNLP Workshop Black-boxNLP: Analyzing and Interpreting Neural Networks for NLP", "volume": "", "issue": "", "pages": "353--355", "other_ids": { "DOI": [ "10.18653/v1/W18-5446" ] }, "num": null, "urls": [], "raw_text": "Alex Wang, Amanpreet Singh, Julian Michael, Fe- lix Hill, Omer Levy, and Samuel Bowman. 2018. GLUE: A multi-task benchmark and analysis plat- form for natural language understanding. In Pro- ceedings of the 2018 EMNLP Workshop Black- boxNLP: Analyzing and Interpreting Neural Net- works for NLP, pages 353-355, Brussels, Belgium. Association for Computational Linguistics.", "links": null }, "BIBREF124": { "ref_id": "b124", "title": "From genesis to creole language: Transfer learning for Singlish Universal Dependencies parsing and pos tagging", "authors": [ { "first": "Hongmin", "middle": [], "last": "Wang", "suffix": "" }, { "first": "Jie", "middle": [], "last": "Yang", "suffix": "" }, { "first": "Yue", "middle": [], "last": "Zhang", "suffix": "" } ], "year": 2019, "venue": "ACM Transactions on Asian and Low-Resource Language Information Processing (TAL-LIP)", "volume": "19", "issue": "1", "pages": "1--29", "other_ids": {}, "num": null, "urls": [], "raw_text": "Hongmin Wang, Jie Yang, and Yue Zhang. 2019. From genesis to creole language: Transfer learning for Singlish Universal Dependencies parsing and pos tagging. ACM Transactions on Asian and Low- Resource Language Information Processing (TAL- LIP), 19(1):1-29.", "links": null }, "BIBREF125": { "ref_id": "b125", "title": "Neural network acceptability judgments", "authors": [ { "first": "Alex", "middle": [], "last": "Warstadt", "suffix": "" }, { "first": "Amanpreet", "middle": [], "last": "Singh", "suffix": "" }, { "first": "Samuel", "middle": [ "R" ], "last": "Bowman", "suffix": "" } ], "year": 2019, "venue": "Transactions of the Association for Computational Linguistics", "volume": "7", "issue": "", "pages": "625--641", "other_ids": {}, "num": null, "urls": [], "raw_text": "Alex Warstadt, Amanpreet Singh, and Samuel R Bow- man. 2019. Neural network acceptability judgments. Transactions of the Association for Computational Linguistics, 7:625-641.", "links": null }, "BIBREF126": { "ref_id": "b126", "title": "A broad-coverage challenge corpus for sentence understanding through inference", "authors": [ { "first": "Adina", "middle": [], "last": "Williams", "suffix": "" }, { "first": "Nikita", "middle": [], "last": "Nangia", "suffix": "" }, { "first": "Samuel", "middle": [], "last": "Bowman", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", "volume": "1", "issue": "", "pages": "1112--1122", "other_ids": {}, "num": null, "urls": [], "raw_text": "Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sen- tence understanding through inference. In Proceed- ings of the 2018 Conference of the North American Chapter of the Association for Computational Lin- guistics: Human Language Technologies, Volume 1 (Long Papers), pages 1112-1122. Association for Computational Linguistics.", "links": null }, "BIBREF127": { "ref_id": "b127", "title": "Transformers: State-of-the-art natural language processing", "authors": [ { "first": "Thomas", "middle": [], "last": "Wolf", "suffix": "" }, { "first": "Lysandre", "middle": [], "last": "Debut", "suffix": "" }, { "first": "Victor", "middle": [], "last": "Sanh", "suffix": "" }, { "first": "Julien", "middle": [], "last": "Chaumond", "suffix": "" }, { "first": "Clement", "middle": [], "last": "Delangue", "suffix": "" }, { "first": "Anthony", "middle": [], "last": "Moi", "suffix": "" }, { "first": "Pierric", "middle": [], "last": "Cistac", "suffix": "" }, { "first": "Tim", "middle": [], "last": "Rault", "suffix": "" }, { "first": "Remi", "middle": [], "last": "Louf", "suffix": "" }, { "first": "Morgan", "middle": [], "last": "Funtowicz", "suffix": "" }, { "first": "Joe", "middle": [], "last": "Davison", "suffix": "" }, { "first": "Sam", "middle": [], "last": "Shleifer", "suffix": "" }, { "first": "Clara", "middle": [], "last": "Patrick Von Platen", "suffix": "" }, { "first": "Yacine", "middle": [], "last": "Ma", "suffix": "" }, { "first": "Julien", "middle": [], "last": "Jernite", "suffix": "" }, { "first": "Canwen", "middle": [], "last": "Plu", "suffix": "" }, { "first": "Teven", "middle": [ "Le" ], "last": "Xu", "suffix": "" }, { "first": "Sylvain", "middle": [], "last": "Scao", "suffix": "" }, { "first": "Mariama", "middle": [], "last": "Gugger", "suffix": "" }, { "first": "", "middle": [], "last": "Drame", "suffix": "" } ], "year": 2020, "venue": "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", "volume": "", "issue": "", "pages": "38--45", "other_ids": { "DOI": [ "10.18653/v1/2020.emnlp-demos.6" ] }, "num": null, "urls": [], "raw_text": "Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier- ric Cistac, Tim Rault, Remi Louf, Morgan Funtow- icz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Trans- formers: State-of-the-art natural language process- ing. In Proceedings of the 2020 Conference on Em- pirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online. Asso- ciation for Computational Linguistics.", "links": null }, "BIBREF128": { "ref_id": "b128", "title": "Quantitative comparative syntax on the Cantonese-Mandarin parallel dependency treebank", "authors": [ { "first": "Tak-Sum", "middle": [], "last": "Wong", "suffix": "" }, { "first": "Kim", "middle": [], "last": "Gerdes", "suffix": "" }, { "first": "Herman", "middle": [], "last": "Leung", "suffix": "" }, { "first": "John", "middle": [], "last": "Lee", "suffix": "" } ], "year": 2017, "venue": "Proceedings of the Fourth International Conference on Dependency Linguistics", "volume": "", "issue": "", "pages": "266--275", "other_ids": {}, "num": null, "urls": [], "raw_text": "Tak-sum Wong, Kim Gerdes, Herman Leung, and John Lee. 2017. Quantitative comparative syntax on the Cantonese-Mandarin parallel dependency tree- bank. In Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017), pages 266-275, Pisa,Italy. Link\u00f6ping Univer- sity Electronic Press.", "links": null }, "BIBREF129": { "ref_id": "b129", "title": "Extended and enhanced Polish dependency bank in Universal Dependencies format", "authors": [ { "first": "Alina", "middle": [], "last": "Wr\u00f3blewska", "suffix": "" } ], "year": 2018, "venue": "Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)", "volume": "", "issue": "", "pages": "173--182", "other_ids": { "DOI": [ "10.18653/v1/W18-6020" ] }, "num": null, "urls": [], "raw_text": "Alina Wr\u00f3blewska. 2018. Extended and enhanced Pol- ish dependency bank in Universal Dependencies for- mat. In Proceedings of the Second Workshop on Uni- versal Dependencies (UDW 2018), pages 173-182, Brussels, Belgium. Association for Computational Linguistics.", "links": null } }, "ref_entries": { "FIGREF0": { "text": "Overview of MACHAMP, when training jointly for sentiment analysis and POS tagging. A shared encoding representation and task-specific decoders are exploited to accomplish both tasks.", "uris": null, "type_str": "figure", "num": null }, "FIGREF1": { "text": "Examples of data file formats.", "uris": null, "type_str": "figure", "num": null }, "FIGREF2": { "text": "Effect of the sampling parameter \u03b1 on the training sets of Universal Dependencies 2.6 data.", "uris": null, "type_str": "figure", "num": null }, "TABREF2": { "text": "", "type_str": "table", "num": null, "html": null, "content": "" }, "TABREF3": { "text": "and are the defaults values for MACHAMP.", "type_str": "table", "num": null, "html": null, "content": "
SetupUD (LAS) GLUE (Acc)
Single72.2282.38
All72.8280.96
Smoothed73.7481.87
Dataset embed. *72.76-
Sep. decoder *73.69-
" }, "TABREF4": { "text": "Average results over all development sets. Dataset embeddings and a separate decoder have not been tested in GLUE, because each dataset is annotated for a different task.", "type_str": "table", "num": null, "html": null, "content": "" }, "TABREF6": { "text": "Mart\u00ednez Alonso and Daniel Zeman. 2016. Universal Dependencies for the AnCora treebanks. Procesamiento del Lenguaje Natural, 57:91-98. Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, and Noah A. Smith. 2016. Many languages, one parser. Transactions of the Association for Computational Linguistics, 4:431-444. Behzad and Amir Zeldes. 2020. A crossgenre ensemble approach to robust Reddit part of speech tagging. In Proceedings of the 12th Web as Corpus Workshop, pages 50-56, Marseille, France. European Language Resources Association. In Proceedings of the Ninth Workshop on Statistical Machine Translation, pages 12-58, Baltimore, Maryland, USA. Association for Computational Linguistics. Guillaume Bonfante, Bruno Guillaume, and Guy Perrier. 2018. Application of Graph Rewriting to Natural Language Processing. Wiley Online Library. Richard Socher. 2017. A joint many-task model: Growing a neural network for multiple NLP tasks. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1923-1933, Copenhagen, Denmark. Association for Computational Linguistics. J\u00f3nsd\u00f3ttir and Anton Karl Ingason. 2020. Creating a parallel Icelandic dependency treebank from raw text to Universal Dependencies. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 2924-2931, Marseille, France. European Language Resources Association.", "type_str": "table", "num": null, "html": null, "content": "
Lars Ahrenberg. 2015. Converting an English-Swedish parallel treebank to Universal Dependencies. In Pro-ceedings of the Third International Conference on Dependency Linguistics (Depling 2015), pages 10-19, Uppsala, Sweden. Uppsala University, Uppsala, Sweden. Linda Alfieri and Fabio Tamburini. 2016. (almost) au-tomatic conversion of the Venice Italian Treebank into the merged Italian Dependency Treebank for-Shabnam Eduard Bej\u010dek, Eva Haji\u010dov\u00e1, Jan Haji\u010d, Pavl\u00edna J\u00ednov\u00e1, V\u00e1clava Kettnerov\u00e1, Veronika Kol\u00e1\u0159ov\u00e1, Marie Mikulov\u00e1, Ji\u0159\u00ed M\u00edrovsk\u1ef3, Anna Nedoluzhko, Jarmila Panevov\u00e1, et al. 2013. Prague dependency treebank 3.0. Luisa Bentivogli, Ido Dagan, Hoa Trang Dang, Danilo Giampiccolo, and Bernardo Magnini. 2009. The fifth PASCAL recognizing textual entailment chal-lenge. In Proceedings of the Second Text Analy-sis Conference, TAC 2009, Gaithersburg, Maryland, USA. Jing Xian Wang, Lucia Lam, Keiko Sophie Mori, Sebastian Garza, and Boris Katz. 2016. Universal Dependencies for learner English. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 737-746, Berlin, Germany. Association for Irshad Bhat, Riyaz A. Bhat, Manish Shrivastava, and Dipti Sharma. 2018. Universal Dependency parsing for Hindi-English code-switching. In Proceedings ter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Pa-pers), pages 987-998, New Orleans, Louisiana. As-sociation for Computational Linguistics. Riyaz Ahmad Bhat, Rajesh Bhatt, Annahita Farudi, Prescott Klassen, Bhuvana Narasimhan, Martha Palmer, Owen Rambow, Dipti Misra Sharma, Ash-wini Vaidya, Sri Ramagurumurthy Vishnu, et al. 2016. The hindi/urdu treebank project. In Hand-book of Linguistic Annotation. Springer Press. Agne Bielinskiene, Loic Boizou, and Jolanta Ko-valevskaite. 2016. Lithuanian dependency treebank. In Human Language Technologies-The Baltic Per-spective: Proceedings of the Seventh International Conference Baltic HLT 2016, volume 289, page 107. IOS Press. Su Lin Blodgett, Johnny Wei, and Brendan O'Connor. African-American and mainstream American En-glish. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Vol-ume 1: Long Papers), pages 1415-1425, Melbourne, Australia. Association for Computational Linguis-tics. Ol\u00e1j\u00edd\u00e9 Ishola and Daniel Zeman. 2020. Yor\u00f9b\u00e1 de-pendency treebank (YTB). In Proceedings of the 12th Language Resources and Evaluation Confer-ence, pages 5178-5186, Marseille, France. Euro-pean Language Resources Association. Tom\u00e1s Jel\u00ednek. 2017. FicTree: A manually annotated 2018. Twitter Universal Dependency parsing for Anton Karl Ingason, Eir\u00edkur R\u00f6gnvaldsson, Einar Freyr Sigurosson, and Joel C. Wallenberg. 2020. The Faroese parsed historical corpus. CLARIN-IS, Stof-nun\u00c1rna Magn\u00fassonar. 119, pages 4411-4421. sociation for Computational Linguistics (Volume 1: Long Papers), pages 328-339, Melbourne, Australia. Association for Computational Linguistics. Junjie Hu, Sebastian Ruder, Aditya Siddhant, Gra-2020. XTREME: A massively multilingual multi-eralization. In Proceedings of the 37th Interna-tional Conference on Machine Learning, volume task benchmark for evaluating cross-lingual gen-ham Neubig, Orhan Firat, and Melvin Johnson. Proceedings of the 56th Annual Meeting of the As-language model fine-tuning for text classification. In Jeremy Howard and Sebastian Ruder. 2018. Universal of the 2018 Conference of the North American Chap-letin of Mathematical Linguistics, 89(1):41-96. Czech academic corpus 2.0 guide. The Prague Bul-Hlav\u00e1\u010dov\u00e1, Ji\u0159\u00ed M\u00edrovsk\u1ef3, and Jan Raab. 2008. The Barbora Hladk\u00e1, Jan Haji\u010d, Jirka Hana, Jaroslava Computational Linguistics. sources Association. Oliver Hellwig, Salvatore Scarlata, Elia Ackermann, 5146, Marseille, France. European Language Re-sources and Evaluation Conference, pages 5137-Sanskrit. In Proceedings of the 12th Language Re-and Paul Widmer. 2020. The treebank of Vedic Yevgeni Berzak, Jessica Kenney, Carolyn Spadine, velopment of a Universal Dependencies treebank for ropean Association for Machine Translation. nology Workshop, pages 21-31, Dublin, Ireland. Eu-Welsh. In Proceedings of the Celtic Language Tech-Johannes Heinecke and Francis M. Tyers. 2019. De-(LaTeCH 2008), pages 27-34. on language technology for cultural heritage data translations. In Proceedings of the second workshop a parallel treebank of the old Indo-European Bible Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsu-ruoka, and Dag TT Haug and Marius J\u00f8hndal. 2008. Creating Dataset RTE MRPC CoLa SST-2 QNLI QQP MNLI MNLI-mis SNLI H\u00e9ctor Angelina Aquino, Franz de Leon, and Mary Ann Bacolod. 2020. UD Tagalog-Ugnayan. https: //github.com/UniversalDependencies/UD_ Tagalog-Ugnayan. Carolina Coelho Aragon. 2018. Varia\u00e7\u00f5es estil\u00edsticas e sociais no discurso dos falantes akunts\u00fa. Polifonia, 25(38.1):90-103. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Ben-gio. 2015. Neural machine translation by jointly learning to align and translate. In 3rd Inter-national Conference on Learning Representations, ICLR 2015, Conference Track Proceedings, San Diego, CA, USA. David Bamman and Gregory Crane. 2011. The ancient Greek and Latin dependency treebanks. In Lan-guage technology for cultural heritage, pages 79-98. Springer. Verginica Barbu Mititelu, Radu Ion, Radu Simionescu, Elena Irimia, and Cenel-Augusto Perez. 2016. The Romanian treebank annotated according to Univer-sal Dependencies. In Proceedings of the tenth inter-national conference on natural language processing (hrtal2016). Tenth International Conference on Machine Learn-ing, pages 41-48, Amherst, MA, USA. Rich Caruana. 1997. Multitask learning. In Learning to learn, pages 95-133. Springer. Flavio Massimiliano Cecchini, Marco Passarotti, Paola man Language Technologies, pages 153-163, Den-guistics. the Association for Computational Linguistics: Hu-burg, Sweden. Association for Computational Lin-2015 Conference of the North American Chapter of pendencies (UDW 2017), pages 102-106, Gothen-multimodal skip-gram model. In Proceedings of the of the NoDaLiDa 2017 Workshop on Universal De-roni. 2015. Combining language and vision with a Universal Dependencies for Greek. In Proceedings Angeliki Lazaridou, Nghia The Pham, and Marco Ba-Prokopis Prokopidis and Haris Papageorgiou. 2017. lenges in annotating and parsing spoken, code-switched, Frisian-Dutch data. In Proceedings of the ral Language Processing. Sabine Brants, Stefanie Dipper, Peter Eisenberg, Sil-via Hansen-Schirra, Esther K\u00f6nig, Wolfgang Lezius, Christian Rohrer, George Smith, and Hans Uszkor-eit. 2004. TIGER: Linguistic interpretation of a ger-man corpus. Research on language and computa-tion, 2(4):597-620. Bernard Caron, Marine Courtin, Kim Gerdes, and Syl-bank for Naija. In Proceedings of the 18th Interna-tional Workshop on Treebanks and Linguistic The-ories (TLT, SyntaxFest 2019), pages 13-24, Paris, France. Association for Computational Linguistics. Rich Caruana. 1993. Multitask learning: A knowledge-based source of inductive bias. In Proceedings of the '01, page 282-289, San Francisco, CA, USA. tics. national Conference on Machine Learning, ICML Germany. Association for Computational Linguis-quence data. In Proceedings of the Eighteenth Inter-(Volume 2: Short Papers), pages 412-418, Berlin, Probabilistic models for segmenting and labeling se-ing of the Association for Computational Linguistics C. N. Pereira. 2001. Conditional random fields: iary loss. In Proceedings of the 54th Annual Meet-John D. Lafferty, Andrew McCallum, and Fernando rectional long short-term memory models and auxil-vain Kahane. 2019. A surface-syntactic UD tree-2016. Multilingual part-of-speech tagging with bidi-Benjamins Publishing Company. Barbara Plank, Anders S\u00f8gaard, and Yoav Goldberg. tactic treebank for spoken French, volume 89. John Pietrandrea. 2019. Rhapsodie: A prosodic and syn-for Computational Linguistics. Anne Lacheret-Dujour, Sylvain Kahane, and Paola 2019), pages 132-136, Paris, France. Association shop on Universal Dependencies (UDW, SyntaxFest 2392. lian treebanking. In Proceedings of the Third Work-Resources and Evaluation (LREC'16), pages 2387-grammars at the same time-an experiment in Kare-of the Tenth International Conference on Language dency treebanks, dictionaries and computational 2016. Czech legal text treebank 1.0. In Proceedings Tommi A Pirinen. 2019. Building minority depen-Vincent Kr\u00ed\u017e, Barbora Hladk\u00e1, and Zdenka Uresova. Second Workshop on Domain Adaptation for Natu-UniversalDependencies/UD_Ukrainian-IU. 2018. UD Ukrainian-IU. https://github.com/ Workshop on Universal Dependencies (UDW 2017), pages 19-26, Gothenburg, Sweden. Association for Computational Linguistics. Anouck Braggaar and Rob van der Goot. 2021. Chal-senko, Snizhana Umanets, and Larysa Masenko. Pareviazko, Yaroslava Rychyk, Anastasiia Stet-Bohdana Matushko, Natalia Onyshchuk, Valeriia Olha Lytvyn, Oksana Orlenko, Hanna Brovko, Romanenko, Halyna Samoridna, Ivanka Kosovska, for Italian, pages 1-8. Pisa University Press. Gosse Bouma and Gertjan van Noord. 2017. Increas-ing return on annotation investment: The automatic for Dutch. In Proceedings of the NoDaLiDa 2017 Natalia Kotsyba, Bohdan Moskalevskyi, Mykhailo construction of a Universal Dependency treebank Akkadian-PISANDUB. com/UniversalDependencies/UD_ PISANDUB. https://github. Kamil Kopacewicz. 2018. UD Akkadian-EVALITA 2014 Evaluation of NLP and Speech Tools Paris, France. Association for Computational Lin-Cristina Bosco, Felice Dell'Orletta, Simonetta Monte-magni, Manuela Sanguinetti, and Maria Simi. 2014. The EVALITA 2014 dependency parsing task. In sociation for Computational Linguistics. IJCNLP), pages 2779-2795, Hong Kong, China. As-ence on Natural Language Processing (EMNLP-Processing and the 9th International Joint Confer-guistics. ence on Empirical Methods in Natural Language universally. In Proceedings of the 2019 Confer-pendencies (UDW, SyntaxFest 2019), pages 46-57, guages, 1 model: Parsing universal dependencies Proceedings of the Third Workshop on Universal De-Dan Kondratyuk and Milan Straka. 2019. 75 lan-Universal Dependencies treebank for German. In nig, and Arne K\u00f6hn. 2019. HDT-UD: A very large Finnish-OOD. Emanuel Borges V\u00f6lker, Maximilian Wendt, Felix Hen-//github.com/UniversalDependencies/UD_ size 2k 4k 9k 67k 105k 364k 393k 393k 550k Single 67.1 85.5 74.7 88.4 85.2 90.5 80.2 80.8 88.9 All 69.3 81.6 70.2 88.2 82.3 90.1 79.2 79.7 88.1 Hildur Jenna Kanerva. 2020. UD Finnish-OOD. https: Smoothed 72.9 82.8 72.7 87.6 83.1 90.3 78.8 80.1 88.4
treebank of Czech fiction. In ITAT, pages 181-185.Marongiu, and Daniel Zeman. 2018. Challenges in ver, Colorado. Association for Computational Lin-Yada Pruksachatkun, Phil Yeres, Haokun Liu, Jason
converting the index Thomisticus treebank into Uni-guistics. Phang, Phu Mon Htut, Alex Wang, Ian Tenney, and
versal Dependencies. In Proceedings of the Sec-Samuel R. Bowman. 2020. jiant: A software toolkit
ond Workshop on Universal Dependencies (UDW John Lee, Herman Leung, and Keying Li. 2017. To-for research on general-purpose text understanding
2018), pages 27-36, Brussels, Belgium. Association wards Universal Dependencies for learner Chinese. models. In Proceedings of the 58th Annual Meet-
for Computational Linguistics. In Proceedings of the NoDaLiDa 2017 Workshop ing of the Association for Computational Linguistics:
" }, "TABREF7": { "text": "", "type_str": "table", "num": null, "html": null, "content": "
: The scores (accuracy) per dataset on the
GLUE tasks (dev) for a variety of multi-task settings
(ordered by size, indicated in number of sentences in
training data).
" } } } }