[PDF] Top 20 Learning Natural Language Inference with LSTM
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Learning Natural Language Inference with LSTM
... In this paper, we proposed a special LSTM ar- chitecture for the task of natural language infer- ence. Based on a recent work by Rockt¨aschel et al. (2016), we first used neural attention models to ... See full document
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Research on attention memory networks as a model for learning natural language inference
... Stanford Natural Language Inference (SNLI) corpus for the purpose of encour- aging more learning centered approaches to NLI (Bowman et ...deep learning methods to solve NLI ...deep ... See full document
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A large annotated corpus for learning natural language inference
... There are revealing patterns in the errors com- mon to all the models considered here. Despite the large size of the training corpus and the distri- butional information captured by GloVe initializa- tion, many lexical ... See full document
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Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference
... markers information for the pretraining of sen- tence encoders. They follow (Kiros et al., 2015) to collect a large sentence pairs corpus from Book- Corpus(Zhu et al., 2015) and propose a sentence representation based on ... See full document
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Explaining Simple Natural Language Inference
... real natural language understanding, as discussed by Condoravdi et ...train learning algorithms and achieve high ...the learning (Gururangan et ... See full document
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A Decomposable Attention Model for Natural Language Inference
... vanilla LSTM encoder, while being triv- ially parallelizable across sentence length, which can allow for considerable speedups in low-latency set- ...complex LSTM-based ... See full document
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Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
... MultiGenre NLI The MultiNLI corpus (Williams et al., 2017) was recently released as a multi-genre version of SNLI. With 433K sentence pairs, MultiNLI improves upon SNLI in its coverage: it contains ten distinct genres of ... See full document
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Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
... as natural language processing, computer vision, and social media inference, one is often interested in learning a Sparse Structured Input-Output Model (SIOM), in which the input variables of ... See full document
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Major-minor long short-term memory for word-level language model
... in natural language processing (NLP) systems like machine translation, speech recognition, learning token embeddings, natural language generation and text ...word-level language ... See full document
15
Survey on Attention Neural Network Models for Natural Language Processing
... ABSTRACT: Natural language processing(NLP) task like machine translation, sentence summarization,sentence pair modeling, paraphrase identification, natural language inference, question ... See full document
5
Natural Language Inference from Multiple Premises
... conditional LSTM (Hochreiter and Schmidhuber, 1997) model of Rockt¨aschel et ...Siamese LSTM network ...first LSTM reads the ...second LSTM, which reads the ... See full document
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Asynchronous Deep Interaction Network for Natural Language Inference
... The other set of models uses the cross-sentence feature or inter-sentence attention from one sen- tence to another, and is hence referred to as a matching-aggregation framework. Parikh et al. (2016) use attention to ... See full document
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Enhanced LSTM for Natural Language Inference
... on natural language inference has been performed on rather small datasets with more con- ventional methods (refer to MacCartney (2009) for a good literature survey), which includes a large bulk of ... See full document
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DR BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
... deep learning archi- tecture to address the natural language in- ference (NLI) ...tional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis ... See full document
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A Phrase Based Alignment Model for Natural Language Inference
... and learning in a simi- lar fashion to MANLI, but uses a simpler, token- based alignment representation, along with a richer set of features for alignment ... See full document
10
Posing Fair Generalization Tasks for Natural Language Inference
... Artificially generated datasets have also been used extensively to gain analytic insights into what models are learning. These methods have the ad- vantage that the complexity of individual exam- ples can be ... See full document
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Using Sentence Level LSTM Language Models for Script Inference
... Mart´ın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Cor- rado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael ... 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 ...of natural logic developed in this paper suc- ceeds in explaining a great variety of everyday pat- terns ... See full document
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Annotation Artifacts in Natural Language Inference Data
... Supervised models leverage annotation arti- facts. Levy et al. (2015) demonstrated that su- pervised lexical inference models rely heavily on artifacts in the datasets, particularly the tendency of some words to ... See full document
6
Context dependent Semantic Parsing for Time Expressions
... We presented the first context-dependent semantic parsing system to detect and resolve time expres- sions. Both models used a Combinatory Catego- rial Grammar (CCG) to construct a set of possible temporal meaning ... See full document
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