Contextual Rephrase Detection for Reducing Friction in Dialogue Systems

Zhuoyi Wang, Saurabh Gupta, Jie Hao, Xing Fan, Dingcheng Li, Alexander Hanbo Li, Chenlei Guo


Abstract
For voice assistants like Alexa, Google Assistant, and Siri, correctly interpreting users’ intentions is of utmost importance. However, users sometimes experience friction with these assistants, caused by errors from different system components or user errors such as slips of the tongue. Users tend to rephrase their queries until they get a satisfactory response. Rephrase detection is used to identify the rephrases and has long been treated as a task with pairwise input, which does not fully utilize the contextual information (e.g. users’ implicit feedback). To this end, we propose a contextual rephrase detection model ContReph to automatically identify rephrases from multi-turn dialogues. We showcase how to leverage the dialogue context and user-agent interaction signals, including the user’s implicit feedback and the time gap between different turns, which can help significantly outperform the pairwise rephrase detection models.
Anthology ID:
2021.emnlp-main.143
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1899–1905
Language:
URL:
https://aclanthology.org/2021.emnlp-main.143
DOI:
10.18653/v1/2021.emnlp-main.143
Bibkey:
Cite (ACL):
Zhuoyi Wang, Saurabh Gupta, Jie Hao, Xing Fan, Dingcheng Li, Alexander Hanbo Li, and Chenlei Guo. 2021. Contextual Rephrase Detection for Reducing Friction in Dialogue Systems. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1899–1905, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Contextual Rephrase Detection for Reducing Friction in Dialogue Systems (Wang et al., EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-main.143.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.143.mp4