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        <title>Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting</title>
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    <abstract>Incomplete utterance rewriting has recently raised wide attention. However, previous works do not consider the semantic structural information between incomplete utterance and rewritten utterance or model the semantic structure implicitly and insufficiently. To address this problem, we propose a QUEry-Enhanced Network(QUEEN) to solve this problem. Firstly, our proposed query template explicitly brings guided semantic structural knowledge between the incomplete utterance and the rewritten utterance making model perceive where to refer back to or recover omitted tokens. Then, we adopt a fast and effective edit operation scoring network to model the relation between two tokens. Benefiting from extra information and the well-designed network, QUEEN achieves state-of-the-art performance on several public datasets.</abstract>
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%0 Conference Proceedings
%T Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting
%A Si, Shuzheng
%A Zeng, Shuang
%A Chang, Baobao
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F si-etal-2022-mining
%X Incomplete utterance rewriting has recently raised wide attention. However, previous works do not consider the semantic structural information between incomplete utterance and rewritten utterance or model the semantic structure implicitly and insufficiently. To address this problem, we propose a QUEry-Enhanced Network(QUEEN) to solve this problem. Firstly, our proposed query template explicitly brings guided semantic structural knowledge between the incomplete utterance and the rewritten utterance making model perceive where to refer back to or recover omitted tokens. Then, we adopt a fast and effective edit operation scoring network to model the relation between two tokens. Benefiting from extra information and the well-designed network, QUEEN achieves state-of-the-art performance on several public datasets.
%R 10.18653/v1/2022.naacl-main.356
%U https://aclanthology.org/2022.naacl-main.356
%U https://doi.org/10.18653/v1/2022.naacl-main.356
%P 4839-4847

Markdown (Informal)

[Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting](https://aclanthology.org/2022.naacl-main.356) (Si et al., NAACL 2022)

• Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting (Si et al., NAACL 2022)

ACL

• Shuzheng Si, Shuang Zeng, and Baobao Chang. 2022. Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4839–4847, Seattle, United States. Association for Computational Linguistics.