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- W2415204069 abstract "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." @default.
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- W2415204069 date "2016-05-29" @default.
- W2415204069 modified "2023-10-16" @default.
- W2415204069 title "Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention" @default.
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- W2415204069 doi "https://doi.org/10.48550/arxiv.1605.09090" @default.
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