@inproceedings{yusupujiang-ginzburg-2023-unravelling,
title = "Unravelling Indirect Answers to Wh-Questions: Corpus Construction, Analysis, and Generation",
author = "Yusupujiang, Zulipiye and
Ginzburg, Jonathan",
editor = "Stoyanchev, Svetlana and
Joty, Shafiq and
Schlangen, David and
Dusek, Ondrej and
Kennington, Casey and
Alikhani, Malihe",
booktitle = "Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2023",
address = "Prague, Czechia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigdial-1.30",
doi = "10.18653/v1/2023.sigdial-1.30",
pages = "336--348",
abstract = "Indirect answers, crucial in human communication, serve to maintain politeness, avoid conflicts, and align with social customs. Although there has been a substantial number of studies on recognizing and understanding indirect answers to polar questions (often known as yes/no questions), there is a dearth of such work regarding wh-questions. This study takes up the challenge by constructing what is, to our knowledge, the first corpus of indirect answers to wh-questions. We analyze and interpret indirect answers to different wh-questions based on our carefully compiled corpus. In addition, we conducted a pilot study on generating indirect answers to wh-questions by fine-tuning the pre-trained generative language model DialoGPT (Zhang et al., 2020). Our results suggest this is a task that GPT finds difficult.",
}
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<abstract>Indirect answers, crucial in human communication, serve to maintain politeness, avoid conflicts, and align with social customs. Although there has been a substantial number of studies on recognizing and understanding indirect answers to polar questions (often known as yes/no questions), there is a dearth of such work regarding wh-questions. This study takes up the challenge by constructing what is, to our knowledge, the first corpus of indirect answers to wh-questions. We analyze and interpret indirect answers to different wh-questions based on our carefully compiled corpus. In addition, we conducted a pilot study on generating indirect answers to wh-questions by fine-tuning the pre-trained generative language model DialoGPT (Zhang et al., 2020). Our results suggest this is a task that GPT finds difficult.</abstract>
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%0 Conference Proceedings
%T Unravelling Indirect Answers to Wh-Questions: Corpus Construction, Analysis, and Generation
%A Yusupujiang, Zulipiye
%A Ginzburg, Jonathan
%Y Stoyanchev, Svetlana
%Y Joty, Shafiq
%Y Schlangen, David
%Y Dusek, Ondrej
%Y Kennington, Casey
%Y Alikhani, Malihe
%S Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F yusupujiang-ginzburg-2023-unravelling
%X Indirect answers, crucial in human communication, serve to maintain politeness, avoid conflicts, and align with social customs. Although there has been a substantial number of studies on recognizing and understanding indirect answers to polar questions (often known as yes/no questions), there is a dearth of such work regarding wh-questions. This study takes up the challenge by constructing what is, to our knowledge, the first corpus of indirect answers to wh-questions. We analyze and interpret indirect answers to different wh-questions based on our carefully compiled corpus. In addition, we conducted a pilot study on generating indirect answers to wh-questions by fine-tuning the pre-trained generative language model DialoGPT (Zhang et al., 2020). Our results suggest this is a task that GPT finds difficult.
%R 10.18653/v1/2023.sigdial-1.30
%U https://aclanthology.org/2023.sigdial-1.30
%U https://doi.org/10.18653/v1/2023.sigdial-1.30
%P 336-348
Markdown (Informal)
[Unravelling Indirect Answers to Wh-Questions: Corpus Construction, Analysis, and Generation](https://aclanthology.org/2023.sigdial-1.30) (Yusupujiang & Ginzburg, SIGDIAL 2023)
ACL