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train_98200 | The impact of the eye-tracking features varies between the different combinations of datasets. | in total, they comprise 142,441 tokens with gaze information. | neutral |
train_98201 | We present the benefits of eyetracking features by evaluating the NER models on both individual datasets as well as in cross-domain settings. | when evaluating the NER models in a cross-corpus scenario, the type-aggregated features lead to significant improvements. | neutral |
train_98202 | The singular unit can thus use these gates to reliably store number information across long-range dependencies. | to gain further insight regarding the functioning of the syntax units, we next visualized their gate and cell dynamics during sentence processing. | neutral |
train_98203 | In this section, we elaborate on the behavior of our model by conducting finer-grained analysis at queue-level and investigating the following questions in the context of challenges of semi-supervised learning. | we analyze our model from various perspectives to explain its improvement gain with respect to challenges of semisupervised learning. | neutral |
train_98204 | Effective semi-supervised learning algorithms based on pretraining techniques (Hinton and Salakhutdinov, 2006;Bengio et al., 2007;Erhan et al., 2010) have been developed for text classification, deep belief networks (Hinton and Salakhutdinov, 2006), and stacked autoencoders Bengio et al., 2007). | the core difference between self-training algorithms is in the second step: data sampling policy. | neutral |
train_98205 | In terms of DANs, we use FastText (Joulin et al., 2017) for its high per-formance and simplicity. | instances in the designated queues show considerably smaller similarity with training instances in both positive and negative classes, and, therefore, do not match well with training data. | neutral |
train_98206 | We replace all target brand names with the keyword BRAND and other non-target brands with BRAND-OTHER for the purpose of our experiments. | if there is a large deviation among movement patterns of instances of the same queue, better data sampling policies could be developed, perhaps through finer-grained queue-level sampling. | neutral |
train_98207 | That is, we examine language model behavior on artificially constructed sentences designed to expose behavior that is crucially dependent on syntactic state representations. | garden-pathing in LSTMs has recently been demonstrated by van Schijndel and Linzen (2018a,b) in the context of modeling human reading times. | neutral |
train_98208 | The N400 is best predicted when the model is trained on that component independently, but every other ERP component prediction can be improved by including a second ERP component in the training. | we logtransform the self-paced reading time and the eyetracking measures. | neutral |
train_98209 | Since this corpus does not include a designated test set, we randomly sampled and removed 200 utterances from training to use as a development set, and use the designated development data as a test set. | transferring the encoder parameters is where most of the benefit comes from. | neutral |
train_98210 | There is a great deal of evidence that many phonological contrasts are perceptually available from a very early stage (Eimas et al., 1971;Moffitt, 1971;Trehub, 1973;Jusczyk and Derrah, 1987;Eimas et al., 1987). | but to prevent the acquisition process from being circular, the learner cannot operate solely on top-down information -the acoustic signal must provide some evidence for the phonemic categories. | neutral |
train_98211 | A few simple examples are given below: Based on the similarity/differences between the reparandum and the repair, disfluencies are often categorized into three types: repetition (the first example), rephrase (the next example), and restart (the last example). | but it 's just you know leak leak leak everywhere people should know that that 's an option and i think you do accomplish more after that i mean [ it was + it ] interesting thing [ about gas is when + i mean about battery powered cars is ] containing 1126 files hand-annotated with disfluencies. | neutral |
train_98212 | Our baselines include models with text-only, prosody cues only (raw), and innovation features only as inputs. | for these types of disfluencies, it makes sense that prosodic cues would not really be needed. | neutral |
train_98213 | The second case is relevant to incident response as modelled by LORELEI (Strassel and Tracey, 2016), where there may only be a single target-language consultant available for which transcribed speech can be elicited, but the goal is to have an ASR model that generalizes to multiple speakers. | choosing as pretraining languages geographically proximal languages tends to help more than phonetically and phonologically similar but otherwise distant languages. | neutral |
train_98214 | Multilingual pretraining of languages from a different language family and script benefitted from an explicit phoneme objective and adversarial objective when there was sufficient diversity in the pretraining languages. | although the characteristics of the speech are unique, it allows us to investigate multilingual models over many languages without the confounds of an overly noisy environment. | neutral |
train_98215 | Optimal word frequency threshold is determined on dev set of each fold. | these features are extracted via sliding a window over the sentence, as displayed in Fig. | neutral |
train_98216 | In this study, we hope to discover the ideal precision and recall tradeoff point regarding cognitive load in CAI terminology assistance and use this feedback to adjust the model. | we would expect loan words, words adopted from a foreign language with little or no modification, to be easier to recognize and translate for an interpreter. | neutral |
train_98217 | The input to our model are mel-frequency cepstral coefficient (MFCC) audio features (Davis and Mermelstein, 1980) and the output is a sequence of words {y m } M m=1 , each of which is a symbol from the dictionary. | our collection induces the annotators to mainly abide to audio, hence, increasing the dependency of written text on the audio input as can be shown in our survey analysis in Figure 5. | neutral |
train_98218 | As an initial exploration of this proposition, we perform various data analysis against the background of humor theories, and we train and examine classifiers to detect humorous edited headlines in our data. | (2015) analyzed cartoon captions in order to understand what made some funnier than others. | neutral |
train_98219 | We use the attention based Transformer (Vaswani et al., 2017) architecture as our baseline. | bERT denoise the 15% of the tokens at random by replacing 80% of them with [MASK], 10% of them with a random word and 10% of them unchanged. | neutral |
train_98220 | Copying mechanism was proved effective on text summarization tasks (See et al., 2017;Gu et al., 2016) and semantic parsing tasks (Jia and Liang, 2016). | the usage of the non-public CLC corpus (Nicholls, 2003) and self-collected non-public error-corrected sentence pairs from Lang-8 made their training data 3.6 times larger than the others and their results hard to compare. | neutral |
train_98221 | Assuming a parsing model is a parameterized representation of a grammar, then we can expect those models to evolve in a similar way. | dravidian and Afro-Asiatic languages are not as consistent. | neutral |
train_98222 | Faroese (fo) is morphologically rich and that should help, however its North-Germanic relatives are morphologically much simpler. | we thus make the simplifying assumption that a language grammar evolves only from an older stage and can be approximated by that previous stage. | neutral |
train_98223 | Assuming that typology is homogeneous in a language family, the phylogeny should drive models to be typologically aware. | at the beginning, we initialize a new blank/random model that will be the basic parsing model for all the world languages. | neutral |
train_98224 | Languages change and evolve over time. | our new training procedure can be applied to any task, so a future work would be to use it to perform phylogenetic POS tagging. | neutral |
train_98225 | We thank Caio Corro, Giorgio Satta, Marco Damonte, as well as NAACL anonymous reviewers for feedback and suggestions. | we refer the reader to Table 5 of Appendix B for the full list of hyperparameters. | neutral |
train_98226 | view of the memory (Section 3). | we used the standard split (sections 2-21 for training, 22 for development and 23 for test). | neutral |
train_98227 | A naïve way of enforcing right-branching guarantee is to do a complete transformation of the subtree on the stack into a right-branching one. | in the worst case the method will reach the bottom of the tree, but often only 3 or 4 nodes need to be transformed to make the tree perfectly the right branching The worst case complexity of repairing the imperfection is O(n) which makes the complexity of the whole parsing algorithm O(n 2 ) for building a single derivation. | neutral |
train_98228 | As visible on the Figure 3a side condition, the lower combinator must not be >B0. | having a special mechanism for right adjunction makes parser both more incremental and more accurate. | neutral |
train_98229 | Formally, has the form: where t is the iteration number, T is the total number of training iterations, f is a monotonically increasing function, and we introduce two new hyper-parameters associated with the cyclical annealing schedule: • M : number of cycles (default M = 4); • R: proportion used to increase within a cycle (default R = 0.5). | this issue causes two undesirable outcomes: (i) an encoder that produces posteriors almost identical to the Gaussian prior, for all observations (rather than a more interesting posterior); and (ii) a decoder that completely ignores the latent variable z, and a learned model that reduces to a simpler language model. | neutral |
train_98230 | compared to the general FST baseline. | to ensure that t 0 defines relation for all possible string pairs (x, y) ∈ Σ * × ∆ * , we add all arcs of the form a = (s 1 , s 1 , ω, σ, δ), ∀(σ, δ) ∈ Σ × ∆ to t . | neutral |
train_98231 | B More analysis on the effectiveness of NFSTs B.1 Does feeding alignments into the decoder help? | in this paper, we propose neural finite state transducers (NFSTs), in which the weight of each path is instead given by some sort of neural network, such as an RNN. | neutral |
train_98232 | The number of paths may be exponential in the size of T , or infinite if T is cyclic. | we use NACS (Bastings et al., 2018) in our experiment. | neutral |
train_98233 | This shows that detecting speech acts is a very challenging task especially for domainindependent environments. | such a dataset can be fed into deep learning algorithms to yield better performance in detecting Answering issues. | neutral |
train_98234 | We had to ensure the simplicity of the task to obtain high quality results. | traditionally, QA has been explored over large textual corpora (Cui et al., 2005;Harabagiu et al., 2001Harabagiu et al., , 2003Ravichandran and Hovy, 2002;Saquete et al., 2009) with answers being textual phrases. | neutral |
train_98235 | Recent efforts have focused on natural language questions as an interface for KBs, where questions are translated to structured queries via semantic parsing (Bao et al., 2016;Bast and Haussmann, 2015;Fader et al., 2013;Mohammed et al., 2018;Reddy et al., 2014;Yang et al., 2014;Yao and Durme, 2014;Yahya et al., 2013). | we do not pair ComQA with a specific knowledge base (KB) or text corpus for answering. | neutral |
train_98236 | The sophistication of the linguistic structure of the questions in the FreebaseQA data set is compared to other similar data sets based on the average length, in number of words, of the questions. | machine learning approaches for NLP are data hungry since they require large amounts of real-world data to train the models for the best possible performance. | neutral |
train_98237 | Targeting on the two main steps, subgraph selection and fact selection, the research community has developed sophisticated approaches. | the reason is that, as opposed to conventional methods which rank the entire subgraph returned from unigram matching to select the top-n candidates, we choose only the first 200 candidates from the subgraph and then rank them with our proposed ranking score. | neutral |
train_98238 | Table 13 shows two major types of error, where the correct answer choice is in bold and the predicted answer choice is in italics. | in this paper, we study question answering on the Ai2 Reasoning Challenge (ARC) scientific QA dataset . | neutral |
train_98239 | MetaQA is a multihop dataset for end-to-end KBQA based on a movie knowledge graph with 43k entities. | we select it to demonstrate that UHop works for long as well as task-specific relations. | neutral |
train_98240 | The current framework uses a greedy search for each single hop. | the number of hops is generally restricted to two or three. | neutral |
train_98241 | To evaluate if the model halts in the search process, we conducted an experiment using PQL3 as the training/validation set and PQL2 as the testing set. | the performance on 3-hop data suffers when trained on 2-hop data. | neutral |
train_98242 | Then contextual-level feature is used to offset the deficiency of GLoVe. | as shown in Table 1, we collected three kinds of results. | neutral |
train_98243 | In many scenarios, we need to comprehend the relationships of entities across documents before answering questions. | figure 1: framework of BAG model. | neutral |
train_98244 | Although Coref-GRU extends GRU with coreference relationships, it is still not enough for multi-hop because hop relationships are not limited to coreference, entities with the same strings also existed across documents which can be used for reasoning. | our BAG model achieves the best performance under all data configurations. | neutral |
train_98245 | k = 10, the VLAWE document representation can grow up to thousands of features, as the number of features is k • d, where d = 300 is the dimensionality of commonly used word embeddings. | our experiments on five benchmark data sets prove that our approach yields competitive results with respect to the state-of-the-art methods. | neutral |
train_98246 | Our approach is inspired by the Vector of Locally-Aggregated Descriptors used for image representation, and it works as follows. | vLAWE is robust to vocabulary distribution gaps between training and test, which can appear when the training set is particularly smaller or from a different domain. | neutral |
train_98247 | System (Poria et al., 2017b) uses contextual information for the prediction but without any attention mechanism. | anger, disgust, happy and sad) for the same utterance. | neutral |
train_98248 | For example, 'text' carries semantic information of the spoken sentence, whereas 'acoustic' information reveals the emphasis (pitch, voice quality) on each word. | similarly, the textual representation of second example "I'm fine.' | neutral |
train_98249 | Why is the experimental result of the BERT-pair model so much better? | our contribution is two-fold: 1. | neutral |
train_98250 | Following the setup of (Hsu and Ku, 2018), in Friends and EmotionPush, we only evaluate the model performance on four emotions: anger, joy, sadness, and neutral, and we exclude the contribution of the rest emotion classes during training by setting their loss weights to zero. | (b) The two variants, HiGRU-f and HiGRU-sf, can further attain +0.9 and +1.5 improvement over Hi-GRU in terms of WA and +1.0 and +1.2 improvement over HiGRU in terms of UWA, respectively. | neutral |
train_98251 | For example, people are usually calm and present a neutral emotion while only in some particular situations, they express strong emotions, like anger or fear. | these models treat texts independently thus cannot capture the inter-dependence of utterances in dialogues (Kim, 2014;Lai et al., 2015;Grave et al., 2017;Chen et al., 2016;Yang et al., 2016). | neutral |
train_98252 | The performance of the sadness emotion is significantly boosted and that on the anger emotion is at least unaffected. | the transcripts do not reveal very strong emotions compared to what the characters might act in the TV show. | neutral |
train_98253 | We find that our joint neural approach to SCL improves unsupervised domain adaptation substantially on a standard sentiment classification task. | the key idea in SCL is that a subset of features, believed to be predictive across domains, are selected as pivot features. | neutral |
train_98254 | Our results also show that while existing pivot selection methods perform well, they are below an oracle-provided ceiling for many source-target pairs for the sentiment classification task we examine. | a neural version of SCL still obtains near state-of-the-art performance (Ziser and Reichart, 2017). | neutral |
train_98255 | In the unsupervised case, such a dictionary can be induced from the monolingual embeddings S and T (Artetxe et al., 2018). | unsupervised BWE methods learn such a mapping without any parallel data. | neutral |
train_98256 | We introduce a new d-dimension vector a p ∈ O = {x ∈ R d | x ≤ 1} to represent the "positive direction", which is to be learned. | we iterates over the following three steps: 1. | neutral |
train_98257 | Following (Artetxe et al., 2018), we first compute the similarity matrices TT , sort them along the second axis and normalize the rows, yielding M s and M t . | our objective is convex with respect to either W s or a p , thus can be efficiently minimized by using the projected gradient descent algorithm. | neutral |
train_98258 | We have also explored the use of sub-word units learned with byte pair encoding (BPE) (Sennrich et al., 2016). | for reasons of space, we report only one example in Table 3, but more examples are available in the supplementary material. | neutral |
train_98259 | In the example, the baseline has chosen a generic word, "program", while ReWE has been capable of correctly predicting "Default Program" and being specific about the object, "it". | as * * The author has changed affiliation to Microsoft after the completion of this work. | neutral |
train_98260 | By applying the two sampling steps described in Section 3.1, about 10M and 6M augmented Ch-En and En-Ru sentences are generated, respectively. | h i,j is calculated as follows: First, a self-attention sub-layer is employed to encode the context. | neutral |
train_98261 | The sentences are extracted from e-commerce websites, in which "subject"s are the goods names shown on a listing page. | to their methods, our method does not make changes to the decoder, and therefore decoding speed remains unchanged. | neutral |
train_98262 | (3) in total. | 7 In both languages, we set the number of clusters to 5000 (a hyperparameter in the algorithm, c = 5000), that is, we will obtain trees with 5000 leaves. | neutral |
train_98263 | Except for layer 1, it is also evident to see larger gaps (more than 20%) at lower layers than higher layers due to the fact that lower layers, which are distant from the topmost loss in the baseline, require more supervision signals to shape their latent representation space. | it is impossible to evaluate the hidden representation on all those tasks; moreover, due to relationship between tokens (Hu et al., 2016) in Y, not all partitions are reasonable. | neutral |
train_98264 | And more similar tasks have closer performances. | we can obtain 4 tasks with s(l) = 5, 8, 11, 20 for the Zh⇒En task and s(l) = 5, 7, 10, 21 for the En⇒De task, where l = 2, 3, 4, 5 of the 6-layer decoder. | neutral |
train_98265 | et al., 2018b), and text-to-text generation evaluation (Birch et al., 2016;Choshen and Abend, 2018;Sulem et al., 2018a). | tUPA often succeeds at making distinctions that are not even encoded in UD. | neutral |
train_98266 | The latter can be achieved using a model trained to reproduce (or mimic) the original embeddings (Pinter et al., 2017). | we then train Mimick (Pinter et al., 2017) as well as both FCM and AM on the skipgram embeddings. | neutral |
train_98267 | We then define the reliability of a context as where is a normalization constant, ensuring that all weights sum to one. | embedding methods generally need many observations of a word to learn a good representation for it. | neutral |
train_98268 | For a comprehensive comparison, it is ideal to have plots for all models. | it identifies and removes style words from texts, searches for related words pertaining to a new target style, and combines the de-stylized text with the search results using a neural model. | neutral |
train_98269 | One reason for this is a lack of suitable evaluation resources. | we used the comparative annotation technique Best-worst Scaling, which addresses the limitations of traditional rating scales. | neutral |
train_98270 | Bigrams (two-word sequences) are especially important there since they are the smallest unit formed by composing words. | the high average relatedness and low standard deviation ( ) for the transpose bigrams, indicate that these pairs tend to be closely related to each other. | neutral |
train_98271 | Observe that among the three methods of word vector representations, the best results are obtained using fastText (word-context matrix factorization model being a close second). | semantically related concepts may not have many properties in common, but there exists some relationship between them which lends them the property of being semantically close. | neutral |
train_98272 | Identifying outliers can lead to better datasets by (1) removing noise in datasets and (2) guiding collection of additional data to fill gaps. | to demonstrate this idea, we developed a novel crowdsourcing pipeline for data collection. | neutral |
train_98273 | One way to better satisfy user intents is by making such processes collaborative (Morris and Horvitz, 2007;Morris, 2013), or conversational (Radlinski and Craswell, 2017). | which advice-seeking question is more likely to have been asked by the narrator: Q1: Is it even possible to be addicted to coffee? | neutral |
train_98274 | In all of the following evaluations, the systems are given the two pools of perspectives U p and evidences U e . | we propose the task of substantiated perspective discovery where, given a claim, a system is expected to discover a diverse set of well-corroborated perspectives that take a stance with respect to the claim. | neutral |
train_98275 | Modeling how visual and linguistic information can jointly contribute to coherent and effective communication is a longstanding open problem with implications across cognitive science. | subjects' intuitions suggest that coherent imagery typically does not contribute instruction content, but rather serves as a visual signal that facilitates inferences that have to be made to carry out the instruction regardless. | neutral |
train_98276 | A crucial limitation to all these approaches lies in the modeling of appropriate historical context, which is simply ignored in most of the works. | first, we use a gating mechanism based on c s and c u that determines the relevance of the previous system utterance in the current turn. | neutral |
train_98277 | We develop CLEVR-Dialog, a large diagnostic dataset for studying multi-round reasoning in visual dialog. | we hope the findings from CLEVR-Dialog will help inform the development of future models for visual dialog. | neutral |
train_98278 | To learn embeddings for verbs and arguments, we extract representations for sentences containing only the word itself. | at the recent VU amsterdam (VUa) metaphor identification shared task (Leong et al., 2018), neural approaches dominated, with most teams using LSTMs trained on word embeddings and additional linguistic features, such as semantic classes and part of speech tags (Wu et al., 2018;Stemle and Onysko, 2018;Mykowiecka et al., 2018;Swarnkar and Singh, 2018). | neutral |
train_98279 | deflate economy) (Rei et al., 2017) or the sentence containing an utterance (Gao et al., 2018). | we concatenate these features into a single feature vector and feed them into a gradient boosting decision tree classifier (Chen and Guestrin, 2016). | neutral |
train_98280 | Table 5 further presents examples requiring at least paragraph-level context, along with gold label and model predictions. | we also observed 4 errors (13%) due to examples with non-verbs and incomplete sentences and 11 examples (35%) where not even paragraph-level context was sufficient for interpretation, mostly in the Conversation genre, demonstrating the subjective and borderline nature of many of the annotations. | neutral |
train_98281 | For example, the word embedding they use (word2vec embedding trained on the Google News dataset 1 (Mikolov et al., 2013)) an-1 https://code.google.com/archive/p/word2vec/ swer the analogy "man is to computer programmer as woman is to x" with "x = homemaker". | if one cannot determine the gender association of a word by looking at its projection on any gendered pair. | neutral |
train_98282 | However, this is not possible when projecting multiple classes into a linear space. | we can observe bias in word embeddings in many different ways. | neutral |
train_98283 | We conducted experiments to verify our method on a benchmark MPQA dataset. | sRL and ORL are highly correlative. | neutral |
train_98284 | For each set of classes, we consider a setting where directed relations are predicted with one where the direction is ignored. | here, we use a discrete emotion categorization scheme based on fundamental emotions as proposed by Plutchik. | neutral |
train_98285 | Full-match evaluation adds nothing to F 1 @5 and F 1 @7 scores. | the system extracts profiles of researchers from digital resources and integrates their data in a common network. | neutral |
train_98286 | 3) of the compressor at t = M + 1 and onward, with the one-hot distribution of the EOS token. | all the RNNs are LSTMs (Hochreiter and Schmidhuber, 1997). | neutral |
train_98287 | Measuring the performance of a summarization system can be done through either automatic or manual evaluation. | in the SCU dataset, we mark the SCUs we used in our experiments, including their grouping as tasks in the system evaluation phase. | neutral |
train_98288 | 3 We test the naive LSH-based partitioning (LSH-only) Reference 4029 3h 12m 32s 1847 24m 21s 10131 22h 48m 08s LSH-only 3694 1s 1752 1s 7827 2s LSH-CW 4085 23s 1875 11s 9861 58s Table 1: Concept mention grouping runtimes on average and for the smallest and largest set. | since comparing neighbors in a sorted list of bit hashes will primarily find those that differ in the last positions, the random permutations are the key part of the algorithm that ensures similar hashes differing at varying positions are found. | neutral |
train_98289 | As shown by previous work, the grouping of coreferent concept mentions across documents is a crucial subtask of it. | that directly leads to a simple O(n) grouping method. | neutral |
train_98290 | 7 The original AllenNLP library uses a byte representation. | 3 as 'dc,''suj,' 'cd,' and 'cpred.' | neutral |
train_98291 | , w i,l i from the root to a leaf. | this can be easily represented as a loop that traverses the input sentence from left to right, linking each word to another from the same sentence or to the dummy root. | neutral |
train_98292 | We gratefully acknowledge NVIDIA Corporation for the donation of a GTX Titan X GPU. | we keep the model that obtains the highest UAS on the development set. | neutral |
train_98293 | When the proper assumptions on the available labels are made, one can typically model the missing labels as latent variables and train a latentvariable conditional random fields model (Quattoni et al., 2005). | due to their uniform assumption on q over the missing labels, these models typically can recall more entities. | neutral |
train_98294 | Their work focused on the citation parsing 3 (i.e., sequence labeling) task which does not suffer from the above issue as no O label is involved. | the model regards the missing labels as latent variables and learns a latent variable CRF using the following loss: the resulting model is called missing label linear-chain CRF (M-CRF) 6 . | neutral |
train_98295 | Actually, "died" and "fired" are the trigger words of Death and Attack events, respectively. | the results of baseline systems are listed in the first group. | neutral |
train_98296 | Building on the baseline above, we establish a new architecture that is able to capture the sub-event types as well as their duration. | we (i) establish a neural baseline that outperforms a graph-based state-of-the-art method for binary sub-event detection (2.7% micro-F 1 improvement), as well as (ii) demonstrate superiority of a recurrent neural network model on the posts sequence level for labeled sub-events (2.4% bin-level F 1 improvement over non-sequential models). | neutral |
train_98297 | The first one, referred to as relaxed evaluation, is commonly used in entity classification tasks (Adel and Schütze, 2017;Bekoulis et al., 2018a,c) and similar to the binary classification baseline system evaluation: score a multibin sub-event as correct if at least one of its comprising bin types (e.g., goal) is correct, assuming that the boundaries are given. | in this work, we propose to use the bin-level evaluation, since it is a more natural way to measure the duration of a sub-event in a supervised sequence labeling setting. | neutral |
train_98298 | Trivial feature augmentation also does not work well, confirming the necessity of learning the graph embedding with GCN. | miwa and Bansal (2016) applied Tree LSTm (Tai et al., 2015) to jointly represent sequences and dependency trees for entity and relation extraction. | neutral |
train_98299 | Hyper-parameters: In our experiments, we use 25 dimensional embedding vectors for the Rowless model, and 12 dimensional embedding vectors for the E-and ENE models. | figure 1 shows the architecture of our model. | neutral |
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