@inproceedings{wang-etal-2021-contextual,
title = "Contextual Rephrase Detection for Reducing Friction in Dialogue Systems",
author = "Wang, Zhuoyi and
Gupta, Saurabh and
Hao, Jie and
Fan, Xing and
Li, Dingcheng and
Li, Alexander Hanbo and
Guo, Chenlei",
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.143",
doi = "10.18653/v1/2021.emnlp-main.143",
pages = "1899--1905",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Contextual Rephrase Detection for Reducing Friction in Dialogue Systems
%A Wang, Zhuoyi
%A Gupta, Saurabh
%A Hao, Jie
%A Fan, Xing
%A Li, Dingcheng
%A Li, Alexander Hanbo
%A Guo, Chenlei
%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 wang-etal-2021-contextual
%X 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.
%R 10.18653/v1/2021.emnlp-main.143
%U https://aclanthology.org/2021.emnlp-main.143
%U https://doi.org/10.18653/v1/2021.emnlp-main.143
%P 1899-1905
Markdown (Informal)
[Contextual Rephrase Detection for Reducing Friction in Dialogue Systems](https://aclanthology.org/2021.emnlp-main.143) (Wang et al., EMNLP 2021)
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.