@inproceedings{hu-etal-2021-context,
title = "Context-Aware Interaction Network for Question Matching",
author = "Hu, Zhe and
Fu, Zuohui and
Yin, Yu and
de Melo, Gerard",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.312",
doi = "10.18653/v1/2021.emnlp-main.312",
pages = "3846--3853",
abstract = "Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations. However, regular cross-attention focuses on word-level links between the two input sequences, neglecting the importance of contextual information. We propose a context-aware interaction network (COIN) to properly align two sequences and infer their semantic relationship. Specifically, each interaction block includes (1) a context-aware cross-attention mechanism to effectively integrate contextual information when aligning two sequences, and (2) a gate fusion layer to flexibly interpolate aligned representations. We apply multiple stacked interaction blocks to produce alignments at different levels and gradually refine the attention results. Experiments on two question matching datasets and detailed analyses demonstrate the effectiveness of our model.",
}
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<abstract>Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations. However, regular cross-attention focuses on word-level links between the two input sequences, neglecting the importance of contextual information. We propose a context-aware interaction network (COIN) to properly align two sequences and infer their semantic relationship. Specifically, each interaction block includes (1) a context-aware cross-attention mechanism to effectively integrate contextual information when aligning two sequences, and (2) a gate fusion layer to flexibly interpolate aligned representations. We apply multiple stacked interaction blocks to produce alignments at different levels and gradually refine the attention results. Experiments on two question matching datasets and detailed analyses demonstrate the effectiveness of our model.</abstract>
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%0 Conference Proceedings
%T Context-Aware Interaction Network for Question Matching
%A Hu, Zhe
%A Fu, Zuohui
%A Yin, Yu
%A de Melo, Gerard
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F hu-etal-2021-context
%X Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations. However, regular cross-attention focuses on word-level links between the two input sequences, neglecting the importance of contextual information. We propose a context-aware interaction network (COIN) to properly align two sequences and infer their semantic relationship. Specifically, each interaction block includes (1) a context-aware cross-attention mechanism to effectively integrate contextual information when aligning two sequences, and (2) a gate fusion layer to flexibly interpolate aligned representations. We apply multiple stacked interaction blocks to produce alignments at different levels and gradually refine the attention results. Experiments on two question matching datasets and detailed analyses demonstrate the effectiveness of our model.
%R 10.18653/v1/2021.emnlp-main.312
%U https://aclanthology.org/2021.emnlp-main.312
%U https://doi.org/10.18653/v1/2021.emnlp-main.312
%P 3846-3853
Markdown (Informal)
[Context-Aware Interaction Network for Question Matching](https://aclanthology.org/2021.emnlp-main.312) (Hu et al., EMNLP 2021)
ACL
- Zhe Hu, Zuohui Fu, Yu Yin, and Gerard de Melo. 2021. Context-Aware Interaction Network for Question Matching. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3846–3853, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.