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train_11400 | Dependency parsing has achieved new states of the art using distributed word representations in neural networks, trained with large amounts of annotated data Ma et al., 2018;Che et al., 2018). | many languages are low-resource, with small or no treebanks, which presents a severe challenge in developing accurate parsing systems in those languages. | contrasting |
train_11401 | Ideally, each word should find itself if it exists in the BERT vocabulary. | this is not always true. | contrasting |
train_11402 | Cotterell and Heigold (2017) train charlevel taggers to predict morphological taggings for high/low resource languages jointly, alleviating OOV problems to some extent. | we focus on dealing with the OOV issue at subword level in the context of pre-trained BERT model. | contrasting |
train_11403 | Early studies on recurrent neural network (RNN)-based model analyze the translation quality with respect to the sentence length, and show that their models improve translations for long sentences, using the long short-term memory (LSTM) (Sutskever et al., 2014) or introducing the attention mechanism (Bahdanau et al., 2015;Luong et al., 2015). | koehn and knowles (2017) report that even RNN-based model with the attention mechanism performs worse than phrase-based statistical machine translation (koehn et al., 2007) in translating very long sentences, which challenges us to develop an NMT model that is robust to long sentences or more generally, variations in input length. | contrasting |
train_11404 | RNN or CNN-based NMT captures relative positions which stem from sequential operation of RNN or convolution operation of CNN. | position embeddings or positional encodings (vector representations of positions) are used to handle absolute positions in Transformer. | contrasting |
train_11405 | 2018propose a RNN-based model, RNMT+, which is based on stacked LSTMs and incorporates some components from Transformer such as layer normalization and multi-head attention. | our model is based on Transformer and incorporates RNN into Transformer. | contrasting |
train_11406 | Transformer shows the worst performance among the four Transformer-based models on the sentences longer than those in the training data for any controlled length. | on the shorter sentences than those in the training data, RNN-Transformer scores almost the same as Transformer on the Middle and Long training data of ASPEC English-to-Japanese and also shows a larger drop than RNN-NMT at length of -24 on the Long training data of WMT2014 English-to-German. | contrasting |
train_11407 | Roy and Pentland (2002) were among the first to propose a computational model, known as CELL, that integrates both speech and vision to study child language acquisition. | cELL required both speech and images to be pre-processed, where canonical shapes were first extracted from images and further represented as histograms; and speech was discretised into phonemes. | contrasting |
train_11408 | We observed the same pattern for the word "fire hydrant" where both "fire hydrant" and "hydrant" are mapped to the same object. | figure 2b shows that when only prompted with /ÃÇ/ the network manages to find pictures of giraffes. | contrasting |
train_11409 | Adding the MFCC vectors corresponding to the transition from /Ç/ to /ae/ triggers a large difference in the embedding as the cosine similarity suddenly jumps to a higher value, showing it is getting closer to its final representation. | cosine similarity plateaus once /ae/ is reached up until final silence, suggesting the final /aef/ plays little to no role in the final representation of the word. | contrasting |
train_11410 | Recently, the creation of large-scale datasets (Bowman et al., 2015;wil;Khot et al., 2018) spurred the development of many neural models (Parikh et al., 2016;Nie and Bansal, 2017;Conneau et al., 2017;Balazs et al., 2017;Chen et al., 2017a;Radford et al., 2018;Devlin et al., 2018). | state-of-the-art models for NLI treat the task like a matching problem, which appears to work in many cases, but breaks down in others. | contrasting |
train_11411 | They propose DROP, a reading comprehension dataset focused on a limited set of discrete operations such as counting, comparison, sorting and arithmetic. | eQUATe features diverse phenomena that occur naturally in text, including reasoning with approximation, ordinals, implicit quantities and quantifiers, requiring NLI models to reason comprehensively about the interplay between quantities and language. | contrasting |
train_11412 | We note from the semantics and annotated examples, we expect this sense of grow to typically be literal. | in the CALIBRATABLE COS-45.6 class, it contains a Value role that moves along a scale by a certain Extent. | contrasting |
train_11413 | Intuitively, we would assume when the auxiliary language has a smaller average distance to all the target languages, the cross-lingual transfer performance would be better. | from the results in Table 6, we do not see such a pattern. | contrasting |
train_11414 | 2013, only assumed that each utterance is generated conditioned on the previous and current topic/DA pairs. | our model is able to model the dependencies of all preceding utterances of a conversation, and hence can better capture the effect between DAs and topics. | contrasting |
train_11415 | Summarization is an important component of strategy instruction in reading and writing skills (Graham and Perin, 2007), but is used less than it could be due to the demands of manual grading and feedback. | integration of NLP with rubric-based assessment has received increasing attention. | contrasting |
train_11416 | These studies rely on human annotation of timestamped subtask goals, e.g., timed captions created through crowdsourcing. | humanin-the-loop annotation is infeasible to deploy for popular video sharing platforms like YouTube that receive hundreds of hours of uploads per minute. | contrasting |
train_11417 | We explore neural encoder-decoder models based on Transformer Networks (Vaswani et al., 2017). | to RNNs, Transformers abandon recurrence in favor of a mix of different types of feedforward layers, e.g., in the case of the Transformer decoder, self-attention layers, cross-attention layers (attending to the encoder outputs), and fully connected feed-forward layers. | contrasting |
train_11418 | determine which object is being referred to. | in many scenarios where a grounding system can be deployed, grounding is not an isolated oneoff task. | contrasting |
train_11419 | ACROSS USERS: Similar to the SAME USER dataset, we create a new example each time the annotator refers to an object. | the past expressions come from the other annotator who was displayed the same scene. | contrasting |
train_11420 | We hypothesize that due to pretrained word embeddings (which include embeddings for words describing the unknown categories), the coreference models will be able to successfully ground to new object categories. | the performance of the Vision model will decrease in the HARD split, because the pretrained visual features of the new objects are not well aligned with representations of unseen words. | contrasting |
train_11421 | We gratefully acknowledge funding support in part by the Honda Research Institute and the Toyota Research Institute. | this article solely reflects the opinions and conclusions of its authors. | contrasting |
train_11422 | Here, we should note that it is often difficult to make sense of these attention weights. | we observe that the attention matrix changes very gradually near the completion of the recipe. | contrasting |
train_11423 | BiDAF w/ static memory is an extended version of the BiDAF model which resembles our proposed PRN model in that it includes a memory unit for the entities. | it does not make any updates on the memory cells. | contrasting |
train_11424 | • Myopicity: The oracle policy chooses the set C i j that maximizes its performance. | the success of LTAL depends on the sequence of choices that are made. | contrasting |
train_11425 | One possibility is that no active learning policy is better than random. | lONGEST outperformed BASEORAClE showing that the problem is at least partially related to BASEORAClE itself. | contrasting |
train_11426 | Because of this, there is no way to pre-fetch the next instance in the background and the annotator has to wait for the selection/generation process to finish before the next instance is presented for annotation. | the runtime for pool-based AL methods is increasing with the pool's size. | contrasting |
train_11427 | However, the runtime for pool-based AL methods is increasing with the pool's size. | the generation method presented in this work does not have this limitation. | contrasting |
train_11428 | One of the main drawbacks of our work is its limitation to binary sentence classification. | multi-class classification in an one-vs-rest schema is compatible with our method and worth further exploration. | contrasting |
train_11429 | In some situations, it is more natural to express the constraints as a set of automata. | naively enforcing multiple automata by fully intersecting them is potentially expensive. | contrasting |
train_11430 | The formulation above assumes that the constraints are described using a single automaton. | in some scenarios, it is more natural to impose multiple constraints by representing them as a set of automata. | contrasting |
train_11431 | While the model can potentially "impose" some constraints which are well-represented in the data, there is no guarantee that the output structure will be valid. | our work guarantees valid output. | contrasting |
train_11432 | (2017) are valuable for the analysis of the fact-checking problem and pro-vide annotations for stance detection. | they contain only several hundreds of validated claims and it is therefore unlikely that deep learning models can generalize to unobserved claims if trained on these datasets. | contrasting |
train_11433 | The corpus therefore allows training deep neural networks for automated fact-checking, which reach higher performance than shallow machine learning techniques. | the corpus is based on synthetic claims derived from Wikipedia sentences rather than natural claims that originate from heterogeneous web sources. | contrasting |
train_11434 | For each claim, the authors extracted about 30 associated documents using the Google search engine, resulting in a collection of 136,085 documents. | since the documents were not annotated by fact-checkers, irrelevant information is present and important information for the claim validation might be missing. | contrasting |
train_11435 | The corpus consists of transcripts of political debates in English and Arabic and provides annotations for two tasks: identification of check-worthy statements (claims) in the transcripts, and validation of 150 statements (claims) from the debates. | as for the corpus PolitiFact17, no evidence for the validation of these claims is available. | contrasting |
train_11436 | The results illustrated in Table 9 show that BertEmb, USE+MLP, BiLSTM, and extendedESIM reach similar performance, with BertEmb being the best. | compared to the FEVER claim validation problem, where systems reach up to 0.7 F1 macro, the scores are relatively low. | contrasting |
train_11437 | In fact, we have performed additional experiments in which we pre-trained a model on the FEVER corpus and fine-tuned the parameters on our corpus and vice versa. | no significant performance gain could be observed in both experiments Based on our analysis, we conclude that heterogeneous data and FGE from unreliable sources, as found in our corpus and in the real world, make it difficult to correctly classify the claims. | contrasting |
train_11438 | Traditional NER for the news domain focuses on three coarsegrained entity types: person, location, and organization (Tjong Kim Sang and De Meulder, 2003). | as NLP technologies have been applied to a broader set of domains, many other entity types have been targeted. | contrasting |
train_11439 | Specifically, in this example, John is labeled as PER, so PER is the only possible correct tag at that position. | lives, in, and Texas are labeled as O (nonentity), which here means only that they may not be PER-but any of them could be LOC, since locations are not annotated for this sentence. | contrasting |
train_11440 | As a result, we demonstrate the first accurate, robust, and highly efficient system that is actually a viable substitute for standard, more cumbersome twostage retrieval and re-ranking systems. | with existing literature, which reports multiple seconds to resolve a single mention, we can provide strong retrieval performance across all 5.7 million Wikipedia entities in around 3ms per mention. | contrasting |
train_11441 | Evaluating word embeddings with cognitive language processing data has been proposed previously. | the available datasets are not large enough for powerful machine learning models, the recording technologies produce noisy data, and most importantly, only few datasets are publicly available. | contrasting |
train_11442 | For instance, we chose a wide selection of eye-tracking features that cover early and late word processing. | choosing only general eye-tracking features such as total reading time would also be a viable strategy. | contrasting |
train_11443 | However, choosing only general eye-tracking features such as total reading time would also be a viable strategy. | the EEG evaluation could be more coarse-grained, one could also try to predict known ERP effects (e.g. | contrasting |
train_11444 | This example consists of a series of events, i.e., "check in (a flight)", "be cleared (at the security)", "wait for (the plane)", etc., which humans who have traveled by plane are very familiar with. | this event sequence appears infrequently in text. | contrasting |
train_11445 | An existing straightforward approach (Peng et al., 2016;Xiong et al., 2016) involves creating a set of relevant entities using an entity linking system to detect and disambiguate the names of entities in a document. | this approach is problematic because (1) entity linking systems produce disambiguation errors (Cornolti et al., 2013), and (2) entities appearing in a document are not necessarily relevant to the given document (Gamon et al., 2013;Dunietz and Gillick, 2014). | contrasting |
train_11446 | MAJ and PARTYM outperform the proposed models in Setting 2. | pARTYM is based on knowledge of the future and so is impractical, and MAJ has no discriminative power for individual politicians. | contrasting |
train_11447 | (2004), an Ideal Point model was trained over both politicians and bills/issues, and at inference time the similarity between politician and bill was used to determine how likely the politician was to vote for the bill. | most politicians have distinct views on different issues, meaning that the views of one politician cannot be captured in a single dimension. | contrasting |
train_11448 | As demonstrated in this paper, this model works well when a politician's voting track record has already been established. | it fails for politicians not in the training data, such as those who have never been elected to office or voted on bills relevant to the issues in the target bill. | contrasting |
train_11449 | Finding such documents is an easy task since search engines are capable of returning doc-uments conveying related information. | if search engines are effective in retrieving these documents, the task of putting them into a coherent picture remains a challenge (Shahaf et al., 2012). | contrasting |
train_11450 | The dataset SLP outputs more segments than the modality version. | most segments do not match the reference. | contrasting |
train_11451 | Neural encoder-decoder models have provided a viable new approach for jointly extracting relations and entity pairs. | these models either fail to deal with entity overlapping among relational facts, or neglect to produce the whole entity pairs. | contrasting |
train_11452 | repeat the process to extract all triplets. | to extract multiple entity pairs for a relation type, both CopyR and HRL have to repeatedly predict the relation type in multiple passes, which is computationally inefficient. | contrasting |
train_11453 | As shown in Table 1, SKE has a richest vocabulary (170,206 tokens), while WebNLG has a smallest vocabulary (5,051 tokens). | to NYT and SKE which has a medium body of relations (24 and 50, respectively), WebNLG has a significantly big body of relations (246 relations). | contrasting |
train_11454 | Distantly-supervised models are popular for this task. | sentences can be long and two entities can be located far from each other in a sentence. | contrasting |
train_11455 | In previous works, the information of entity types are commonly utilized to benefit event detection. | the sequential features of entity types have not been well utilized yet in the existing ED methods. | contrasting |
train_11456 | Similar to the word sequences, entity type sequences, which consist of entity type annotations for each token in the word sequences, also contain sequential features, because the position of an entity mention's type in the sequences may affect its importance in the ED process. | to the best of our knowledge, there is no study which regards the entity type sequence as an independent sequence to capture the sequential features and discusses what influence the entity types' sequential features would take to the ED task. | contrasting |
train_11457 | To summarize, on the one hand, ETEED significantly improves the recall values compared with the state-of-the-art methods. | eTeeD produces relatively comparable precisions to the existing methods, which ensures the good F1-score. | contrasting |
train_11458 | As methods which use argument role information, ETEED argument significantly outperforms TEACHER, this result further proves the effectiveness of our model. | in the real testing and application scenarios, we can hardly obtain the arguments role information before getting the event types of the candidate triggers. | contrasting |
train_11459 | Chambers and Jurafsky (2011) design a weakly supervised system, which can automatically induce the event templates and extract event information from unlabeled corpus, to alleviate the need of expert knowledge. | the required external resources are not always available for some low-resource languages. | contrasting |
train_11460 | (2017) utilize the entity type embedding directly as local context of the current word, and calculate the attention values between them; others (Liu et al., 2018b(Liu et al., , 2019Nguyen and Grishman, 2018) concatenate the entity type embeddings with the work token embeddings, in order to integrate these two types of features into mixed representations with the help of neural networks. | these existing studies ignore the entity types' sequential features which may benefit the ED task. | contrasting |
train_11461 | Recent developments in Named Entity Recognition (NER) have resulted in better and better models. | is there a glass ceiling? | contrasting |
train_11462 | We decided not to open the whole data set, because it is the test set and the tuning models on this set would lead to unfair results. | we could not perform the analysis on a validation set because it is rather poor with respect to different kinds of linguistic properties. | contrasting |
train_11463 | Named entity classification as an instance of PU learning was introduced in Grave (2014), which uses constrained optimization with constraints similar to ours. | they only address the problem of named entity classification, in which mentions are given, and the goal is to assign a type to a named-entity (like 'location', 'person', etc.) | contrasting |
train_11464 | One contribution of this work is the inference step (line 6), which we address using a constrained Integer Linear Program (ILP) and describe in this section. | the constraints are based on a value we call the entity ratio. | contrasting |
train_11465 | A similar assumption was made in Elkan and Noto (2008) when determining the c value, and in Grave (2014) in the constraint determining the percentage of OTHER examples. | we also show in Section 4.8 that knowledge of this ratio is not strictly necessary, and a flat value across all datasets produces similar performance. | contrasting |
train_11466 | In all our experiments so far, we have used the gold entity ratio for each language, as shown in Table 1. | exact knowledge of entity ratio is unlikely in the absence of gold data. | contrasting |
train_11467 | Recently, contextualized Bidirectional Encoder Representations from Transformers (BERT) models have established state-of-the-art performance for a variety of NLP tasks. | there has not been much effort in exploring language transfer using BERT for event extraction. | contrasting |
train_11468 | For ZH, we obtain F1-scores of 84.4% and 79.9%, amounting to an increase of 16.2% and 16.9% over the previous state-of-the-art. | although results using Bi-LSTM-Char-CRF lag behind state-of-the-art for EN, incurring a loss of 10.5% over trigger classification, they are competitive for ZH, with scores of 86.6% and 69.5% and gains of 17.9% and 6.2% over Feng's HNN for trigger identification and classification respectively. | contrasting |
train_11469 | (2016) exploit both languagedependent and language-independent features in the form of universal features such as universal dependencies, limited bilingual dictionaries and aligned multilingual word embeddings to train a model with multiple languages. | this work lags behind in terms of the neural approach used and doesn't investigate the effectiveness of leveraging multiple source languages. | contrasting |
train_11470 | More recently, neural network-based methods have been employed for event temporal relation extraction (Tourille et al., 2017a;Cheng and Miyao, 2017;Meng et al., 2017;Han et al., 2019a) which achieved impressive results. | they all treat the task as a pairwise classification problem. | contrasting |
train_11471 | We hypothesize that this might be due to some sport specific documents that make roughly 1/4 of the dataset's mentions. | without spoiling the test-set we cannot know for sure. | contrasting |
train_11472 | We found that with our approach we can learn additional entity knowledge in BERT that helps in entity linking. | we also found that almost none of the downstream tasks really required entity knowledge, which is an interesting observation and an open question for future research. | contrasting |
train_11473 | Since we focus on domain adaptation and used very simple encoders, we do not attempt to achieve state-of-the-art (e.g., Dai and Huang (2018), Bai and Zhao (2018)). | this performance is on-par with many recent work using multi-task or GANs, including Lan et al. | contrasting |
train_11474 | Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. | it is still difficult to interpret the predictions of existing MRC models. | contrasting |
train_11475 | For extractive MRC tasks, information retrieval techniques can be very strong baselines to extract sentences that contain questions and their answers when questions provide sufficient information, and most questions are factoid and answerable from the content of a single sentence (Lin et al., 2018;Min et al., 2018). | we face unique challenges to extract evidence sentences for multiple-choice MRC tasks. | contrasting |
train_11476 | Several work also investigate content selection at the token level (Yu et al., 2017;Seo et al., 2018), in which some tokens are automatically skipped by neural models. | they do not utilize any linguistic knowledge, and a set of discontinuous tokens has limited explanation capability. | contrasting |
train_11477 | Their ability to understand user spoken commands and identify the user's intent(s) is one of their main benefits. | this is a very challenging task due to the diversity of domains and languages they are required to support. | contrasting |
train_11478 | Existing language modeling (LM) based approaches such as Para2Vec (Le and Mikolov, 2014) rely on deep neural networks to generate vector representations for the paragraph-level. | these approaches are normally trained with an abundance of utterances to achieve the required performance, which is usually not present in the conversational text domain (Boyanov et al., 2017). | contrasting |
train_11479 | A recent approach proposed by (Le and Mikolov, 2014) generates both word-level and paragraph-level representations. | the approach relies on training the network with a large corpus with billions of tokens, which is rarely available in the conversational text domain. | contrasting |
train_11480 | Thus, the resulting vector is closer to neighbors of both points, shrinking the distances between neighbors of both utterances.Moreover, because each feature (dimension) in the space is a representative utterance, after the collapsing of two utterances, we perform PCA to come up with the new dimensions after the user is done with the labeling. | whenever a "Cannot-Link" constraint is provided, the similarity score between the two utterances is set to zero in the corresponding entry in SimMatrix. | contrasting |
train_11481 | (2016b) learn a fixed vector for each person from all conversational texts in the training corpus. | as a global representation, the fixed person vector needs to be trained from largescale dialogue turns for each interlocutor, and it may have a sparsity issue since some interlocutors have very few dialogue turns. | contrasting |
train_11482 | Therefore, it is hard to learn a global vector for each interlocutor from the sparse corpus. | our ICRED performs well on such a sparse corpus (details in Section 4.5). | contrasting |
train_11483 | Memory Graph Networks (MGN) (Section 2.2): Many previous work in QA or MRC systems use memory networks to evaluate multiple answer candidates with transitive reasoning, and typically store all potentially relevant raw sentences or bag-of-symbols as memory slots. | naive increase of memory slot size or retentionbased sequential update of memory slots often increase search space for answer candidates, leading to poor precision especially for the Episodic Memory QA task. | contrasting |
train_11484 | The sentence lacks the semantics needed to fully understand "club and country". | if we follow the URL in the original text, we can get additional information to assist with the understanding. | contrasting |
train_11485 | As the setup of such a text forecasting task is essentially similar to summarizing future text data, we use two popular evaluation metrics from the literature of text summarization, i.e., BLEU (Papineni et al., 2002) and ROUGE (Lin, 2004), where a score is generated by comparing the automatically generated text against some reference text written by humans. | neither BLEU nor ROUGE considers the notion of time, thus we need a time-sensitive customization of both BLEU and ROUGE. | contrasting |
train_11486 | The learning objective of most sequence-to-sequence models is to minimize the negative log likelihood of the generated sequence as shown in following equation, where y * t is the t-th ground-truth summary token. | with this objective, traditional sequence generation models consider only one direction context in the decoding process, which could cause performance degradation since complete context of one token contains preceding and following tokens, thus feeding only preceded decoded words to the decoder so that the model may generate unnatural sequences. | contrasting |
train_11487 | During pre-training, they are fed with complete sequences. | with a left-context-only decoder, these pretrained language models will suffer from incomplete and inconsistent context and thus cannot generate good enough context-aware word representations, especially during the inference process. | contrasting |
train_11488 | 7shows, the decoder's learning objective is to minimize negative likelihood of conditional probability, in which y * t is the t-th ground truth word of summary. | a decoder with this structure is not sufficient enough: if we use the BERT network in this decoder, then during training and inference, in-complete context(part of sentence) is fed into the BERT module, and although we can finetune BERT's parameters, the input distribution is quite different from the pre-train process, and thus harms the quality of generated context representations. | contrasting |
train_11489 | Integrating HRED with the latent variable models such as variational autoencoder (VAE) (Kingma and Welling, 2014) extends another line of advancements (Serban et al., 2016c;Zhao et al., 2017;Park et al., 2018;Le et al., 2018b). | these systems are not designed for taskoriented dialogue modeling as goal information is not considered. | contrasting |
train_11490 | Goal-embedded LM is the best on precision and F1 with G-DuHA having com- parable performance. | even thought LM can better mention the slots in dialogue generation, utterances are often associated with a wrong role. | contrasting |
train_11491 | This implies hierarchical structures capturing longer dependencies can make up the disadvantages of having no goal information for response generation. | as illustrated in Table 12, HRED could still fail to predict the switch of domain contexts, e.g. | contrasting |
train_11492 | The evaluator also employs structure-sensitive rewards based on evaluation measures such as BLEU, GLEU, and ROUGE-L, which are suitable for QG. | most of the previous works only optimize the cross-entropy loss, which can induce inconsistencies between training (objective) and testing (evaluation) measures. | contrasting |
train_11493 | Model BLEU-1 BLEU-2 BLEU-3 BLEU-4 METEOR ROUGE-L L2A (Du et al., 2017) 43 (Paulus et al., 2018) 11.20-3.50-1.21-0.45-6.68-15.25-SUM ROUGE (Paulus et al., 2018) 11.94-3. shown in Table 3, seven of our eight models outperform the two baselines, with GE DAS+QSS+ANSS being the best model on syntactic correctness and semantic correctness quality metrics, outperforming all the other models by a large margin. | model GE BLEU+QSS+ANSS generates highly relevant questions and is the best model on relevance metrics. | contrasting |
train_11494 | In summarization, one generates and paraphrases sentences that capture salient points of the text. | generating questions additionally involves determining question type such as what, when, etc., being selective on which keywords to copy from the input into the question, leaving remaining keywords for the answer. | contrasting |
train_11495 | It finally outputs the hypothesis with the maximum probability of generation. | we observe a reasonable difference in the formality scores among hypotheses with similar generation probabilities. | contrasting |
train_11496 | Like greedy decoding, beam search is a likelihood-maximizing decoding algorithmgiven the input sequence x, the objective is to find an output sequence y which maximizes P (y|x). | researchers have shown that for openended generation tasks (including storytelling), beam search produces repetitive, generic and degenerate text (Holtzman et al., 2019). | contrasting |
train_11497 | 12 This implies that, as with lexical diversity, the models have no difficulty fitting the statistical distribution of human syntax. | under likelihood-maximizing decoding algorithms such as low k, a completely different distribution emerges, in which text contains more verbs and pronouns than human text, and fewer nouns, adjectives and proper nouns. | contrasting |
train_11498 | As shown in the left plot of Figure 4, the gradients of the "x + F" structure are basically the same, indicating that all the residual blocks have similar speed for gradient descent and optimization. | the right plot of Figure 4 reflects that more gradients are allocated to the lower blocks with the help of SAS, and the overall gradient values are greater. | contrasting |
train_11499 | Hence, the task of German NER has benefited from these developments. | with respect to the availability of a variety of resources, there has not been much progress made until now. | contrasting |
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