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        <title>Element-wise Bilinear Interaction for Sentence Matching</title>
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    <name type="personal">
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            <title>Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics</title>
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            <namePart type="given">Malvina</namePart>
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            <namePart type="given">Jonathan</namePart>
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    <abstract>When we build a neural network model predicting the relationship between two sentences, the most general and intuitive approach is to use a Siamese architecture, where the sentence vectors obtained from a shared encoder is given as input to a classifier. For the classifier to work effectively, it is important to extract appropriate features from the two vectors and feed them as input. There exist several previous works that suggest heuristic-based function for matching sentence vectors, however it cannot be said that the heuristics tailored for a specific task generalize to other tasks. In this work, we propose a new matching function, ElBiS, that learns to model element-wise interaction between two vectors. From experiments, we empirically demonstrate that the proposed ElBiS matching function outperforms the concatenation-based or heuristic-based matching functions on natural language inference and paraphrase identification, while maintaining the fused representation compact.</abstract>
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%0 Conference Proceedings
%T Element-wise Bilinear Interaction for Sentence Matching
%A Choi, Jihun
%A Kim, Taeuk
%A Lee, Sang-goo
%Y Nissim, Malvina
%Y Berant, Jonathan
%Y Lenci, Alessandro
%S Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F choi-etal-2018-element
%X When we build a neural network model predicting the relationship between two sentences, the most general and intuitive approach is to use a Siamese architecture, where the sentence vectors obtained from a shared encoder is given as input to a classifier. For the classifier to work effectively, it is important to extract appropriate features from the two vectors and feed them as input. There exist several previous works that suggest heuristic-based function for matching sentence vectors, however it cannot be said that the heuristics tailored for a specific task generalize to other tasks. In this work, we propose a new matching function, ElBiS, that learns to model element-wise interaction between two vectors. From experiments, we empirically demonstrate that the proposed ElBiS matching function outperforms the concatenation-based or heuristic-based matching functions on natural language inference and paraphrase identification, while maintaining the fused representation compact.
%R 10.18653/v1/S18-2012
%U https://aclanthology.org/S18-2012
%U https://doi.org/10.18653/v1/S18-2012
%P 107-112

Markdown (Informal)

[Element-wise Bilinear Interaction for Sentence Matching](https://aclanthology.org/S18-2012) (Choi et al., *SEM 2018)

• Element-wise Bilinear Interaction for Sentence Matching (Choi et al., *SEM 2018)

ACL

• Jihun Choi, Taeuk Kim, and Sang-goo Lee. 2018. Element-wise Bilinear Interaction for Sentence Matching. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 107–112, New Orleans, Louisiana. Association for Computational Linguistics.