Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention

2016-05-30 · Yang Liu, Chengjie Sun, Lei Lin, Xiaolong Wang

In this paper, we proposed a sentence encoding-based model for recognizing text entailment. In our approach, the encoding of sentence is a two-stage process. Firstly, average pooling was used over word-level bidirectional LSTM (biLSTM) to generate a first-stage sentence representation. Secondly, attention mechanism was employed to replace average pooling on the same sentence for better representations. Instead of using target sentence to attend words in source sentence, we utilized the sentence's first-stage representation to attend words appeared in itself, which is called "Inner-Attention" in our paper . Experiments conducted on Stanford Natural Language Inference (SNLI) Corpus has proved the effectiveness of "Inner-Attention" mechanism. With less number of parameters, our model outperformed the existing best sentence encoding-based approach by a large margin.

📄 PDF Abstract BibTeX arXiv:1605.09090

Code (2)

Smerity/keras_snli

songyang0716/NLP/blob/master/natural_language_inference/BiLSTM_encoders_with_intra-attention/model.py pytorch

Tasks

Natural Language InferenceSentence

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