@inproceedings{kikteva-etal-2024-question,
title = "Question Type Prediction in Natural Debate",
author = "Kikteva, Zlata and
Trautsch, Alexander and
Herbold, Steffen and
Hautli-Janisz, Annette",
editor = "Kawahara, Tatsuya and
Demberg, Vera and
Ultes, Stefan and
Inoue, Koji and
Mehri, Shikib and
Howcroft, David and
Komatani, Kazunori",
booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2024",
address = "Kyoto, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sigdial-1.53",
pages = "624--630",
abstract = "In spontaneous natural debate, questions play a variety of crucial roles: they allow speakers to introduce new topics, seek other speakers{'} opinions or indeed confront them. A three-class question typology has previously been demonstrated to effectively capture details pertaining to the nature of questions and the different functions associated with them in a debate setting. We adopt this classification and investigate the performance of several machine learning approaches on this task by incorporating various sets of lexical, dialogical and argumentative features. We find that BERT demonstrates the best performance on the task, followed by a Random Forest model enriched with pragmatic features.",
}
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<abstract>In spontaneous natural debate, questions play a variety of crucial roles: they allow speakers to introduce new topics, seek other speakers’ opinions or indeed confront them. A three-class question typology has previously been demonstrated to effectively capture details pertaining to the nature of questions and the different functions associated with them in a debate setting. We adopt this classification and investigate the performance of several machine learning approaches on this task by incorporating various sets of lexical, dialogical and argumentative features. We find that BERT demonstrates the best performance on the task, followed by a Random Forest model enriched with pragmatic features.</abstract>
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%0 Conference Proceedings
%T Question Type Prediction in Natural Debate
%A Kikteva, Zlata
%A Trautsch, Alexander
%A Herbold, Steffen
%A Hautli-Janisz, Annette
%Y Kawahara, Tatsuya
%Y Demberg, Vera
%Y Ultes, Stefan
%Y Inoue, Koji
%Y Mehri, Shikib
%Y Howcroft, David
%Y Komatani, Kazunori
%S Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2024
%8 September
%I Association for Computational Linguistics
%C Kyoto, Japan
%F kikteva-etal-2024-question
%X In spontaneous natural debate, questions play a variety of crucial roles: they allow speakers to introduce new topics, seek other speakers’ opinions or indeed confront them. A three-class question typology has previously been demonstrated to effectively capture details pertaining to the nature of questions and the different functions associated with them in a debate setting. We adopt this classification and investigate the performance of several machine learning approaches on this task by incorporating various sets of lexical, dialogical and argumentative features. We find that BERT demonstrates the best performance on the task, followed by a Random Forest model enriched with pragmatic features.
%U https://aclanthology.org/2024.sigdial-1.53
%P 624-630
Markdown (Informal)
[Question Type Prediction in Natural Debate](https://aclanthology.org/2024.sigdial-1.53) (Kikteva et al., SIGDIAL 2024)
ACL
- Zlata Kikteva, Alexander Trautsch, Steffen Herbold, and Annette Hautli-Janisz. 2024. Question Type Prediction in Natural Debate. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 624–630, Kyoto, Japan. Association for Computational Linguistics.