Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting

Shuzheng Si, Shuang Zeng, Baobao Chang


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.
Anthology ID:
2022.naacl-main.356
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4839–4847
Language:
URL:
https://aclanthology.org/2022.naacl-main.356
DOI:
10.18653/v1/2022.naacl-main.356
Bibkey:
Cite (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.
Cite (Informal):
Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting (Si et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.356.pdf