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train_16400 | Probably because of this, there is very little literature or understanding of the effect of order among the training examples-strict ordering of examples has simply been known to be undesirable. | as neural network approaches increasingly dominate in performance across many NLP tasks, the notion of random shuffling has become overshadowed by that of computational efficiency. | contrasting |
train_16401 | They empirically compare their efficiency on two translation tasks and find that some strategies in wide use are not necessarily optimal for accuracy and convergence-wise. | to the work described here however, one of the sorting strategies produced best results, though no comparison was made with the original ordering of examples. | contrasting |
train_16402 | We use a simple neural entailment model, Decomposable Attention (Parikh et al., 2016), one of the state-of-the-art models on the SNLI entailment dataset (Bowman et al., 2015). | our architecture can just as easily use any other neural entailment model. | contrasting |
train_16403 | The task of the Aggregator network is to combine these to produce a single entailment score. | we found that using only the final predictions from the three modules was not effective. | contrasting |
train_16404 | Neural models have shown several state-ofthe-art performances on Semantic Role Labeling (SRL). | the neural models require an immense amount of semantic-role corpora and are thus not well suited for lowresource languages or domains. | contrasting |
train_16405 | Improvements of SRL system via use of syntactic constraints is consistent with other observations (Punyakanok et al., 2008). | all previous works enforce syntactic consistency only during decoding step. | contrasting |
train_16406 | The performance of the structured prediction drops rapidly when the noise in the parse information is introduced (x column of Table 4). | ssL was trained on CoNLL2012 data where about 10% of the gold sRL-spans do not match with gold parse-spans and even when we increase noise level to 20% the performance drop was only around 0.1 F1 score. | contrasting |
train_16407 | Users of the system typically have a general understanding of its purpose, so the input will revolve around the correct topic of air travel. | they are unlikely to know the limits of the system's functionality, and may provide inputs for which the expected action is beyond its capabilities, such as asking to change seats on a flight reservation. | contrasting |
train_16408 | One might attempt self-training (McClosky et al., 2006), where new instances are generated by applying a trained model to unannotated data, using high-confidence predictions as ground truth labels. | in such a scheme, the expectation is that the unlabelled text already contains errors, which is not usually the case for most freely available text such as Wikipedia articles as they strive towards correctness. | contrasting |
train_16409 | This approach follows the previous works on grammar induction using non-neural models where the entire dataset is used for training (Klein and Manning, 2002). | this implies that the parsing results of PRPN-UP may not be generalizable in the way usually expected of machine learning evaluation results. | contrasting |
train_16410 | We would expect a similar result for Telugu. | telugu treebank is entirely composed of sentences from a grammar book which may not be expressive and diverse. | contrasting |
train_16411 | It's not hard to imagine how such systems could be useful. | to generic text summarization, RC systems could answer targeted questions about specific documents, efficiently extracting facts and insights. | contrasting |
train_16412 | On first glance the the length-20 passages in CBT, might suggest that success requires reasoning over all 20 sentences to identify the correct answer to each question. | it turns out that for some models, comparable performance can be achieved by considering only the last sentence. | contrasting |
train_16413 | Such approach ensures a diversity and full coverage of all possible dialogue outcomes within a certain domain. | the naturalness of the dialogue flows relies entirely on the engineered set-up of the user and system bots. | contrasting |
train_16414 | At each generation step, only the already produced sequence is taken into account. | future and not-yet-produced tokens can also be highly relevant when choosing the current token. | contrasting |
train_16415 | The self-attention module of a Transformer network treats a sequence bidrectionally as a fully connected graph of tokens -when a token is produced all other tokens are taken into consideration. | this requires the entire sequence to be known a priori and when a Transformer is used for sequence generation, the self-attention process only includes previously produced tokens (Vaswani et al. | contrasting |
train_16416 | (2017), Jain and Wallace's requisite for attention distributions to be used as explanation is that there must only exist one or a few closely-related correct explanations for a model prediction. | doshi-Velez and Kim (2017) caution against applying evaluations and terminology broadly without clarifying taskspecific explanation needs. | contrasting |
train_16417 | sampled data to evaluate the comparative effectiveness of different AL strategies. | collection of such additional data would defeat the purpose of AL, i.e., obviating the need for a large amount of supervision. | contrasting |
train_16418 | This coupling is problematic because manually labeled data tends to have a longer shelf life than models, largely because it is expensive to acquire. | progress in machine learning is fast. | contrasting |
train_16419 | For example, AL methods -both in the standard and acquisition/successor settings -perform much more reliably on the Subjectivity dataset than any other. | aL performs consistently poorly on the TREC dataset. | contrasting |
train_16420 | Specifically, deep learning models, which became a prominent tool in many data driven tasks in recent years, require large datasets to work well. | many tasks require manual annotations which are relatively hard to obtain at scale. | contrasting |
train_16421 | Mann and McCallum (2007) and following work take the base classifier p θ (y|x) to be a logistic regression classifier, for which they manually derive gradients for the XR loss and train with LBFGs (Byrd et al., 1995). | nothing precludes us from using an arbitrary neural network instead, as long as it culminates in a softmax layer. | contrasting |
train_16422 | For each KB, we first use an integrated entity linker to retrieve relevant entity embeddings, then update contextual word representations via a form of word-to-entity attention. | to previous approaches, the entity linkers and selfsupervised language modeling objective are jointly trained end-to-end in a multitask setting that combines a small amount of entity linking supervision with a large amount of raw text. | contrasting |
train_16423 | For a typical 25 token sentence, approximately 2M entity embedding parameters are actually used. | bERT LARGE uses the majority of its 336M parameters for each input. | contrasting |
train_16424 | For example, if word vectors were perfectly isotropic (i.e., directionally uniform), then SelfSim (w) = 0.95 would suggest that w's representations were poorly contextualized. | consider the scenario where word vectors are so anisotropic that any two words have on average a cosine similarity of 0.99. | contrasting |
train_16425 | As noted earlier, contextualized representations are more contextspecific in upper layers of ELMo, BERT, and GPT-2. | how does this increased contextspecificity manifest in the vector space? | contrasting |
train_16426 | We also experiment with two correlation measures: Pearson's (r) and Kendall's rank (τ ) correlation coefficients. | to linear regression and Pearson's correlation coefficient, Kendall's tau is non-parametric and resistant to outliers (Kendall, 1948). | contrasting |
train_16427 | Consistently, on vectors with few outliers (word2vec), Pearson's r achieves the same performance as rank correlations even without winsorization. | unlike outliers, positive (negative) skew of max-(min-) pooled vectors does not seem to hurt Pearson's r on STS tasks. | contrasting |
train_16428 | A typical reward function on policy learning consists of a small negative penalty at each turn to encourage a shorter session, and a large positive reward when the session ends successfully if the agent completes the user goal. | specifying an effective reward function is challenging in task-oriented dialog. | contrasting |
train_16429 | For example, it is inappropriate to book a 3-star hotel without confirming with the user at the first turn in Table 1. | an explicit user goal is essential to evaluate the task success in the reward design, but user goals are hardly available in real situations (Su et al., 2016). | contrasting |
train_16430 | We also observe that GP-MBCM tries to provide many dialog acts to avoid the negative penalty at each turn, which results in a very low inform F1 and short dialog turns. | as explained in the introduction, a shorter dialog is not always the best. | contrasting |
train_16431 | Referring to Figure 3 (a), only using hop1 selector is not better than using multiple selectors. | the performance does not increase when k > 3. | contrasting |
train_16432 | Intuitively, when γ is too large, the selectors will filter out too much context, which may hurt performance. | when γ is too small, the selectors do not work very well. | contrasting |
train_16433 | Previous research on empathetic dialogue systems has mostly focused on generating responses given certain emotions. | being empathetic not only requires the ability of generating emotional responses, but more importantly, requires the understanding of user emotions and replying appropriately. | contrasting |
train_16434 | Table 1 shows an conversation from the empathetic-dialogues dataset (Rashkin et al., 2018) about how an empathetic person would respond to the stressful situation the Speaker has been through. | despite the importance of empathy and emotional understanding in human conversations, it is still very challenging to train a dialogue agent able to recognize and respond with the correct emotion. | contrasting |
train_16435 | With the additional supervision on user emotions, multi-task training improves both Empathy and Relevance score, but it still degrades Fluency. | moEL achieves the highest Empathy and Relevance score. | contrasting |
train_16436 | For example, in the fifth example in Figure 4, the model fails to detect the real emotion of speaker as the context contains "I was pretty surprised" in its last turn. | the last three rows of the heatmap indicate that the model learns to leverage multiple listeners to produce an empathetic response. | contrasting |
train_16437 | As mentioned in section 3.3.1, we adopt the memory network to train our KB-retriever. | in the Seq2Seq dialogue generation, the training data does not include the annotated KB row retrieval results, which makes supervised training the KBretriever impossible. | contrasting |
train_16438 | A common practice is to apply RL on a neural sequence-to-sequence (seq2seq) framework with the action space being the output vocabulary in the decoder. | it is difficult to design a reward function that can achieve a balance between learning an effective policy and generating a natural dialog response. | contrasting |
train_16439 | Hence, we feed the conversation to a bidirectional gated recurrent unit (GRU) (Chung et al., 2014). | like most of the current models, we also ignore intent modelling, topic, and personality due to lack of labelling on those aspects in the benchmark datasets. | contrasting |
train_16440 | In theory, RNNs like long short-term memory (LSTM) (Hochreiter and Schmidhuber, 1997) and GRU should propagate long-term contextual information. | in practice it is not always the case (Bradbury et al., 2017). | contrasting |
train_16441 | Finally, utterance representation is obtained by feeding the current utterance as query to two distinct memory networks for both speakers. | this model can only model conversations with two speakers. | contrasting |
train_16442 | The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. | enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. | contrasting |
train_16443 | One major reason is that learning long dependencies using gated RNNs may not be effective enough because the gradients are expected to propagate back through inevitably a huge number of utterances and tokens in sequence, which easily leads to the vanishing gradient problem (Bengio et al., 1994). | when the utterance-level LSTM in cLSTM is replaced by features extracted by CNN, i.e., the CNN+cLSTM, the model performs significantly better than cLSTM on long conversations, which further validates that modelling long conversations using only RNN models may not be sufficient. | contrasting |
train_16444 | Our KET variants KET SingleSelfAttn and KET StdAttn perform comparably with the best baselines on all datasets except IEMOCAP. | both variants perform noticeably worse than KET on all datasets except EC, validating the importance of our proposed hierarchical self-attention and dynamic context-aware affective graph attention mechanism. | contrasting |
train_16445 | A relevant emotion ranking framework was proposed in (Yang et al., 2018) to predict multiple relevant emotions as well as the rankings based on their intensities. | existing emotion detection approaches do not model the events in texts which are crucial for emotion detection. | contrasting |
train_16446 | proposed a model with several constraints using non-negative matrix factorization based on emotion lexicon for multiple emotion detection. | these approaches often suffer from low recall. | contrasting |
train_16447 | In particular, the attention-based recurrent neural networks (RNNs) (Schuster and Paliwal, 2002;Yang et al., 2016) prevail in text classification. | these approaches ignore the latent events in texts thus fail to attend on event-related parts. | contrasting |
train_16448 | (2019b) generated tips by considering 'persona' information which can capture the language style of users and characteristics of items. | these works use whole reviews or tips as training examples, which may not be appropriate due to the quality of review text. | contrasting |
train_16449 | Unfortunately, most websites do not provide such finegrained information. | our work identifies justifications from reviews, uses them as training examples and shows these are better data source for explainable recommendation via extensive experiments. | contrasting |
train_16450 | Although recent studies on multi-task learning framework suggest that closely related tasks can improve each other mutually from separated supervision information (Ma et al., 2018;Cerisara et al., 2018;, the acquisition of sentence (or utterance)-level sentiment labels, which is required by multi-task learning, remains a laborious and expensive endeavor. | coarse-grained document (or dialogue)-level annotations are relatively easy to obtain due to the widespread use of opinion grading interfaces (e.g., ratings). | contrasting |
train_16451 | Recently, Multiple Instance Learning (MIL) framework is adopted for performing documentlevel and sentence-level sentiment classification simultaneously while only using document-level sentiment annotations (Zhou et al., 2009;Wang and Wan, 2018). | these models are trained based on plain textual data which are in a much simpler form than our multi-turn dialogue structure. | contrasting |
train_16452 | More recently, some researchers started to explore the utterance-level structure for sentiment classification, such as modeling dialogues via a hierarchical RNN in both word level and utterance level (Cerisara et al., 2018) or keeping track of sentiment states of dialogue participants (Majumder et al., 2018). | none of these works can do dialogue-level satisfaction classification and utterance-level sentiment classification simultaneously. | contrasting |
train_16453 | In the simplest case, satisfaction polarities C = {well satisfied, met, unsatisfied} can be computed by averaging all predicted sentiment distributions of customer utterances as y = 1 M t∈[1,M ] p ct . | it is a crude way of combining sentiment distributions uniformly, as not all distributions convey equally important sentiment clues. | contrasting |
train_16454 | In-depth Analysis: CAMIL f ull is only trained based on satisfaction labels, thus the laborious acquisition of sentiment labels is unnecessary. | we would point out that lack of sentiment labels will inevitably lead to difficulties on identifying positive/negative utterances from those neutral ones. | contrasting |
train_16455 | Here our baseline system takes the standard approach, using the 1-best parser output tree D T as features. | our proposed model uses the most confident parser forest D F as features. | contrasting |
train_16456 | On the one hand, keeping the whole search space gives 100% recall, but introduces maximum noise. | using the 1-best dependency tree can result in low recall given an imperfect parser. | contrasting |
train_16457 | For connectivity, KBESTEISNER guarantees to generate spanning forests. | the connectivity ratio for the forests produced by EDGEWISE drops when increasing the threshold . | contrasting |
train_16458 | The main reason may be that EDGEWISE generates denser forests, providing richer features. | kBESTEISNER shows a marginal improvement by increasing k from 5 to 10. | contrasting |
train_16459 | These methods take advantage of supervised or distantlysupervised data to learn neural sentence encoders for distributed representations, and have achieved promising results. | these methods cannot handle the open-ended growth of new relation types in the open-domain corpora. | contrasting |
train_16460 | Finally, we want to explain the reason why we do not use some other common clustering methods like K-Means, Mean-Shift and Ward's (Ward Jr, 1963) method of HAC: these methods calculate the centroid of several points during clustering by merely averaging them. | the relation vectors in our model are high-dimensional, and the distance metric described by RSN is non-linear. | contrasting |
train_16461 | A previous OpenRE work reports performance on an unpublic dataset called NYT-FB (Marcheggiani and Titov, 2016). | it has several shortcomings compared with FewRel-distant. | contrasting |
train_16462 | Recent developments in the field often take an embeddingbased approach to model the structural information of KGs so that entity alignment can be easily performed in the embedding space. | most existing works do not explicitly utilize useful relation representations to assist in entity alignment, which, as we will show in the paper, is a simple yet effective way for improving entity alignment. | contrasting |
train_16463 | There exist a variety of methods (Bordes et Socher et al., 2013;Wang et al., 2014;Trouillon et al., 2016;Nguyen et al., 2018) that have been proposed to learn good embeddings for entities and relations. | embedding of uncommon relation or entities can not learn a good representation due to the data insufficiency. | contrasting |
train_16464 | During meta-training phase, it takes O as inputs and learns latent patterns for triplets. | during the training stage of the meta-testing phase, instead of learning latent patterns, the triplet generator performs triplet aug- Step 1: encoding process of relation descriptions Step Step 2: computation of entity traits mentation by generating extra K sets of embeddings G = {(g h , g r , g t )}. | contrasting |
train_16465 | In KG, if an entity is involved in multiple relations, it is natural that different relations are more relevant to different parts in the description of the entity. | existing works using textual descriptions have not tackled this issue effectively. | contrasting |
train_16466 | We can construct them from online resources, such as the anchors in Wikipedia. | the following natures of WL data make learning name tagging from them more challenging: Partially-Labeled Sequence Automatically derived WL data does not contain complete annotations, thus can not be directly used for training. | contrasting |
train_16467 | Another line of work is to replace CRFs with Partial-CRFs (Täckström et al., 2013), which assign unlabeled words with all possible labels and maximize the total probability Shang et al., 2018). | they still rely on seed annotations or domain dictionaries for high-quality training. | contrasting |
train_16468 | According to a manually defined mapping Γ(Y) → C (e.g., Γ(ORG) =Organizations), we denote all the classes and their children with the same type (e.g., ORG). | there are two minor issues. | contrasting |
train_16469 | On one hand, it predicts each word's label separately, naturally addressing the issue of inconsecutive labels. | we only focus on the labeled words, so that the module is robust to the noise since most noise arises from the unlabeled words, and enjoy an efficient training procedure. | contrasting |
train_16470 | Prior distribution and local contexts, either in the form of hand-crafted features (Ratinov et al., 2011;Shen et al., 2015) or dense embeddings (He et al., 2013;Nguyen et al., 2016;Francis-Landau et al., 2016), play key roles in distinguishing different candidates. | in many cases, local features can be too sparse to provide sufficient information for disambiguation. | contrasting |
train_16471 | To alleviate this problem, various collective EL models have been proposed to globally optimize A traditional global EL model jointly optimizes the linking configuration after iterative calculations over all mentions, which is computationally expensive. | the DCA process only requires one pass of the document to accumulate knowledge from previously linked mentions to enhance fast future inference. | contrasting |
train_16472 | entity disambiguation for dynamic web data like Twitter). | the computational complexity of our model is O(T × |E| × I × K), where K is the key hyper-parameter described in Section 3 and is usually set to a small number. | contrasting |
train_16473 | Thus, such mentions will always introduce wrong information to the model, which leads to a worse performance. | the AIDA-B dataset does not have such situations. | contrasting |
train_16474 | As Figure (4.c) shows, when |E| increases, the running time of these two global EL models increases shapely, while our DCA model grows linearly. | we also observed that the resources required by the DCA model are insensitive to |E|. | contrasting |
train_16475 | Both approximation methods iteratively improve the global assignment, but are still computationally expensive with unbounded number of iterations. | the proposed DCA method only requires one pass through the document. | contrasting |
train_16476 | We adopt graph convolutional networks (GCN) (Kipf and Welling, 2017; Marcheggiani and Titov, 2017) to encode graph structures over the input data, applying graph convolution operations to generate entity and word representations in a latent space. | to other encoders such a Tree-LSTM (Tai et al., 2015), GCN can cover more complete contextual information from dependency parses because, for each word, it captures all parse tree neighbors of the word, rather than just the child nodes of the word. | contrasting |
train_16477 | Mean-selector (Lin et al., 2016;Ye et al., 2017): The bag encoding is computed as Max-selector (Jiang et al., 2016): The j th element of bag encoding x is computed as Attention-selector Attention mechanism is extensively used for sentence selection in relation extraction by weighted summing of the sentence vectors, such as in (Lin et al., 2016;Ye et al., 2017;Su et al., 2018). | all these works assume that the labels are correct and only use the golden label embeddings to select sentences at training stage. | contrasting |
train_16478 | For the first event, the entity 1 "[SHARE1]" is the correct Pledged Shares at the sentence level (ID 5). | due to the capital stock increment (ID 7), Figure 2: A document example with two Equity Pledge event records whose arguments scatter across multiple sentences, where we use ID to denote the sentence index, substitute entity mentions with corresponding marks, and color event arguments outside the scope of key-event sentences as red. | contrasting |
train_16479 | The most recent work, DCFEE , attempted to explore DEE on ChFinAnn, by employing distant supervision (DS) (Mintz et al., 2009) to generate EE data and performing a two-stage extraction: 1) a sequence tagging model for SEE, and 2) a key-event-sentence detection model to detect the key-event sentence, coupled with a heuristic strategy that padded missing arguments from surrounding sentences, for DEE. | the sequence tagging model for SEE cannot handle multi-event sentences elegantly, and even worse, the context-agnostic argumentscompletion strategy fails to address the argumentsscattering challenge effectively. | contrasting |
train_16480 | On the other hand, another recent work (Zeng et al., 2018b) showed that directly labeling event arguments without trigger words was also feasible. | they only considered the SEE setting and their methods cannot be directly extended to the DEE setting, which is the main focus of this work. | contrasting |
train_16481 | (2018) proposes the nugget proposal networks (NPN) in terms of this issue, which uses a neural network to model character compositional structure of trigger words in a fix-sized window. | the mechanism of the NPN limits the scope of trigger candidates within a fix-sized window, which is inflexible and suffering from the problem of trigger overlaps. | contrasting |
train_16482 | All approaches except ours give lower recall rates in the trigger-mismatch part than in the trigger-match part. | our model could robustly address the word-trigger mismatch problem, reaching the best results on both parts of the two datasets. | contrasting |
train_16483 | These methods have achieved great success in English datasets. | in languages without delimiters, such as Chinese, the mismatch of word-trigger become significantly severe. | contrasting |
train_16484 | (2018) proposes NPN, a neural network based method to address the issue. | the mechanism of NPNs limits the scope of trigger candidates within a fix-sized window, which will cause two problems in the progress. | contrasting |
train_16485 | But in this work, word disambiguation datasets are necessary. | our model can solve both word-trigger mismatch and trigger polysemy problems at the same time. | contrasting |
train_16486 | For example, a token can be tagged with B-P ER, where B indicates the boundary of an entity and P ER indicates the corresponding entity categorical label. | when entities are nested within one another, single-layer sequence labeling models can not ex-tract both entities simultaneously. | contrasting |
train_16487 | To generate a triplet, they first generated the relation, then they copy the first entity and the second entity from the source sentence. | none of them have considered the extraction order of multiple triplets in a sentence. | contrasting |
train_16488 | It ranks all the entities in the graph for their likelihood to be the missing entity and the rank assigned to the true missing entity is considered. | while this is suitable for ontological KGs, it is not valid for our setting. | contrasting |
train_16489 | Table 1 shows that in both the datasets, on an average, the number of train triples for each NP and RP is less than 2. | fB15k (Bordes et al., 2013), an ontological KG, has on an average 32 triples for each entity and 360 triples for each relation. | contrasting |
train_16490 | All the above RE models are supervised machine learning models that need to be trained with large amounts of manually annotated RE data to achieve high accuracy. | annotating RE data by human is expensive and timeconsuming, and can be quite difficult for a new language. | contrasting |
train_16491 | Spanish, Italian, Portuguese, German (in conventional typology) and Chinese also belong to the SVO language family, and our approach achieves over 70% relative accuracy for these languages. | japanese belongs to the SOV (Subject, Object, Verb) language family and Arabic belongs to the VSO (Verb, Subject, Object) language family, and our approach achieves lower relative accuracy for these two languages. | contrasting |
train_16492 | The conventional distant supervision strategy only exploits instances that directly mention a target entity pair, and because of this, we refer to it as 1-hop distant supervision. | there are a large number of Web tables that contain relational facts about entities (Cafarella et al., 2008;Venetis et al., 2011;Wang et al., 2012). | contrasting |
train_16493 | To the best of our knowledge, there has been no existing work on joint event-temporal relation extraction. | the idea of "joint" has been studied for entityrelation extraction in many works. | contrasting |
train_16494 | For densely annotated data, the micro-average metric should share the same precision, recall and F1 scores. | since our joint model includes NONE pairs, we follow the convention of IE tasks and exclude them from evaluation. | contrasting |
train_16495 | HMCN also has the same issue, resulting in Macro-F1 lower than 10 when combining with some base models. | hiLAP outperforms the baselines significantly in Macro-F1, which implies that our method is bet- Table 4: Performance comparison on Functional Catalogue and Gene Ontology. | contrasting |
train_16496 | We found, for example, 29,186/781,265 (3.74%) predictions of TextCNN have inconsistent on RCV1. | hiLAP ensures 0% label inconsistency without the need of post-processing, because its predictions are always valid sub-trees of the label hierarchy (refer to Fig. | contrasting |
train_16497 | Normally, each word's representation is constructed by counting all its context features. | for the polysemic word which contains multiple senses, the context features of different senses are mixed together, leading to inaccurate word representation. | contrasting |
train_16498 | Moreover, the accuracy was further improved by attention-based neural networks [Lin et al., 2017;Vaswani et al., 2017;Yang et al., 2016]. | these models are less efficient than capsule networks. | contrasting |
train_16499 | The word 'wonder' in the first sample sentence means something that fills you with surprise and admiration, which shows a very positive sentiment. | the polysemic word 'wonder' in the second and third sentences means to think about something and try to decide what is true, which is neutral in sentiment. | contrasting |
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