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        <title>Inter-Weighted Alignment Network for Sentence Pair Modeling</title>
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    <abstract>Sentence pair modeling is a crucial problem in the field of natural language processing. In this paper, we propose a model to measure the similarity of a sentence pair focusing on the interaction information. We utilize the word level similarity matrix to discover fine-grained alignment of two sentences. It should be emphasized that each word in a sentence has a different importance from the perspective of semantic composition, so we exploit two novel and efficient strategies to explicitly calculate a weight for each word. Although the proposed model only use a sequential LSTM for sentence modeling without any external resource such as syntactic parser tree and additional lexicon features, experimental results show that our model achieves state-of-the-art performance on three datasets of two tasks.</abstract>
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%0 Conference Proceedings
%T Inter-Weighted Alignment Network for Sentence Pair Modeling
%A Shen, Gehui
%A Yang, Yunlun
%A Deng, Zhi-Hong
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F shen-etal-2017-inter
%X Sentence pair modeling is a crucial problem in the field of natural language processing. In this paper, we propose a model to measure the similarity of a sentence pair focusing on the interaction information. We utilize the word level similarity matrix to discover fine-grained alignment of two sentences. It should be emphasized that each word in a sentence has a different importance from the perspective of semantic composition, so we exploit two novel and efficient strategies to explicitly calculate a weight for each word. Although the proposed model only use a sequential LSTM for sentence modeling without any external resource such as syntactic parser tree and additional lexicon features, experimental results show that our model achieves state-of-the-art performance on three datasets of two tasks.
%R 10.18653/v1/D17-1122
%U https://aclanthology.org/D17-1122
%U https://doi.org/10.18653/v1/D17-1122
%P 1179-1189

Markdown (Informal)

[Inter-Weighted Alignment Network for Sentence Pair Modeling](https://aclanthology.org/D17-1122) (Shen et al., EMNLP 2017)

• Inter-Weighted Alignment Network for Sentence Pair Modeling (Shen et al., EMNLP 2017)

ACL

• Gehui Shen, Yunlun Yang, and Zhi-Hong Deng. 2017. Inter-Weighted Alignment Network for Sentence Pair Modeling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1179–1189, Copenhagen, Denmark. Association for Computational Linguistics.