[PDF] Top 20 A Decomposable Attention Model for Natural Language Inference
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A Decomposable Attention Model for Natural Language Inference
... Natural language inference (NLI) refers to the prob- lem of determining entailment and contradiction re- lationships between a premise and a ...in language understanding (Katz, 1972; Bos and ... See full document
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Neural Models for Detecting Binary Semantic Textual Similarity for Algerian and MSA
... ious attention mechanisms have been proposed to deal with more than one semantic similarity task at the same ...apply attention to represent mutual influence be- tween the input sentence ...the ... See full document
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Semantic Sentence Matching with Densely-Connected Recurrent and Co-Attentive Information
... our model on the natural language inference task over SNLI and MultiNLI ...our model with other published ...from language models as an ex- ternel ...ensemble model ... See full document
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Saama Research at MEDIQA 2019: Pre trained BioBERT with Attention Visualisation for Medical Natural Language Inference
... a language rep- resentation model which performs on a wide range of NLP tasks such as question answer- ing and language ...trained language representations include feature- based (ELMO) ... See full document
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Learning Natural Language Inference with LSTM
... The second limitation is that the model by Rockt¨aschel et al. (2016) does not explicitly allow us to place more emphasis on the more important matching results between the premise and the hy- pothesis and ... See full document
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Natural Language and Inference in a Computer Game
... some model, subsumption is to decide of two concepts whether all individuals that belong to one concept must necessarily belong to another, and instance and relation checking test whether an individual belongs to ... See full document
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Survey on Attention Neural Network Models for Natural Language Processing
... Graph attention network deploys multiple convolution layer and inductive ...graph attention network uses convolution layer for aggregation of neighboring node features and applying a non linear function to ... See full document
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Enhanced LSTM for Natural Language Inference
... as natural language inference could well involve both, which has been discussed in the context of rec- ognizing textual entailment (RTE) (Mehdad et ...local inference modeling and ... See full document
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Annotation Artifacts in Natural Language Inference Data
... lexical inference models rely heavily on artifacts in the datasets, particularly the tendency of some words to serve as prototypical ...InferSent model (Conneau et ... See full document
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Modeling Semantic Containment and Exclusion in Natural Language Inference
... because natural logic has a weaker proof theory than FOL, some inferences lie beyond its deductive ...the model of natural logic developed in this paper suc- ceeds in explaining a great variety of ... See full document
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An Attention Based Command Detection Model to Allow Natural Language in Voice Control System
... giving natural language instructions and measured the car’s ...detection model can translate various oral expressions (of the same meaning) into the same ... See full document
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Natural Language Inference from Multiple Premises
... the attention model outperformed both LSTM and SE models in overall accuracy, it is not the best in every ...and attention have access to the same information, but the attention model ... See full document
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Compositional Lexical Semantics In Natural Language Inference
... the language component of natural language inference, so that one can reason about how natural language expressions relate to one ...“many natural language ... See full document
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Explaining Simple Natural Language Inference
... the inference relation be- tween a pair of sentences, it is unclear which phe- nomena are also difficult for a human to ...receives attention when dealing with inference ... See full document
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Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension
... the model via carefully de- signed experiments, several interactive demo/vi- sualization systems, such as AllenNLP’s demos ...the model pre- ...and attention component in neural machine translation ... See full document
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Interpreting Recurrent and Attention Based Neural Models: a Case Study on Natural Language Inference
... Attention has been widely used in many NLP tasks (Ghaeini et al., 2018a; Dhingra et al., 2017; Bah- danau et al., 2014) and is probably one of the most critical parts that affects the inference deci- sions. ... See full document
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A Phrase Based Alignment Model for Natural Language Inference
... in inference than nouns and verbs, they are not irrelevant, and because sentences often contain multiple instances of a par- ticular function word, matching them properly is by no means ... See full document
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Character level Intra Attention Network for Natural Language Inference
... BiLSTM model architecture In the baseline model, a word embedding layer initialized with pre-trained GloVe vectors (840B token version) is implemented to transform the input text into sequence of word ... See full document
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Decomposable Modeling in Natural Language Processing
... In lO-fold cross-validations, the model search procedure performs significantly better than naive Bayes on 6 of the words without being significantly worse on any of them... The class of[r] ... See full document
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Research on attention memory networks as a model for learning natural language inference
... is the weights corresponding to element-wise differ- ence). Thus, our third heuristic can be absorbed in- to the first one in terms of model capacity. How- ever, as will be shown in the experiment, explic- itly ... See full document
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