@inproceedings{wu-etal-2023-focus,
title = "Focus-aware Response Generation in Inquiry Conversation",
author = "Wu, Yiquan and
Lu, Weiming and
Zhang, Yating and
Jatowt, Adam and
Feng, Jun and
Sun, Changlong and
Wu, Fei and
Kuang, Kun",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.797",
doi = "10.18653/v1/2023.findings-acl.797",
pages = "12585--12599",
abstract = "Inquiry conversation is a common form of conversation that aims to complete the investigation (e.g., court hearing, medical consultation and police interrogation) during which a series of focus shifts occurs. While many models have been proposed to generate a smooth response to a given conversation history, neglecting the focus can limit performance in inquiry conversation where the order of the focuses plays there a key role. In this paper, we investigate the problem of response generation in inquiry conversation by taking the focus into consideration. We propose a novel Focus-aware Response Generation (FRG) method by jointly optimizing a multi-level encoder and a set of focal decoders to generate several candidate responses that correspond to different focuses. Additionally, a focus ranking module is proposed to predict the next focus and rank the candidate responses. Experiments on two orthogonal inquiry conversation datasets (judicial, medical domain) demonstrate that our method generates results significantly better in automatic metrics and human evaluation compared to the state-of-the-art approaches.",
}
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<abstract>Inquiry conversation is a common form of conversation that aims to complete the investigation (e.g., court hearing, medical consultation and police interrogation) during which a series of focus shifts occurs. While many models have been proposed to generate a smooth response to a given conversation history, neglecting the focus can limit performance in inquiry conversation where the order of the focuses plays there a key role. In this paper, we investigate the problem of response generation in inquiry conversation by taking the focus into consideration. We propose a novel Focus-aware Response Generation (FRG) method by jointly optimizing a multi-level encoder and a set of focal decoders to generate several candidate responses that correspond to different focuses. Additionally, a focus ranking module is proposed to predict the next focus and rank the candidate responses. Experiments on two orthogonal inquiry conversation datasets (judicial, medical domain) demonstrate that our method generates results significantly better in automatic metrics and human evaluation compared to the state-of-the-art approaches.</abstract>
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%0 Conference Proceedings
%T Focus-aware Response Generation in Inquiry Conversation
%A Wu, Yiquan
%A Lu, Weiming
%A Zhang, Yating
%A Jatowt, Adam
%A Feng, Jun
%A Sun, Changlong
%A Wu, Fei
%A Kuang, Kun
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wu-etal-2023-focus
%X Inquiry conversation is a common form of conversation that aims to complete the investigation (e.g., court hearing, medical consultation and police interrogation) during which a series of focus shifts occurs. While many models have been proposed to generate a smooth response to a given conversation history, neglecting the focus can limit performance in inquiry conversation where the order of the focuses plays there a key role. In this paper, we investigate the problem of response generation in inquiry conversation by taking the focus into consideration. We propose a novel Focus-aware Response Generation (FRG) method by jointly optimizing a multi-level encoder and a set of focal decoders to generate several candidate responses that correspond to different focuses. Additionally, a focus ranking module is proposed to predict the next focus and rank the candidate responses. Experiments on two orthogonal inquiry conversation datasets (judicial, medical domain) demonstrate that our method generates results significantly better in automatic metrics and human evaluation compared to the state-of-the-art approaches.
%R 10.18653/v1/2023.findings-acl.797
%U https://aclanthology.org/2023.findings-acl.797
%U https://doi.org/10.18653/v1/2023.findings-acl.797
%P 12585-12599
Markdown (Informal)
[Focus-aware Response Generation in Inquiry Conversation](https://aclanthology.org/2023.findings-acl.797) (Wu et al., Findings 2023)
ACL
- Yiquan Wu, Weiming Lu, Yating Zhang, Adam Jatowt, Jun Feng, Changlong Sun, Fei Wu, and Kun Kuang. 2023. Focus-aware Response Generation in Inquiry Conversation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12585–12599, Toronto, Canada. Association for Computational Linguistics.