@inproceedings{nath-etal-2024-thoughts,
title = "{\textquotedblleft}Any Other Thoughts, Hedgehog?{\textquotedblright} Linking Deliberation Chains in Collaborative Dialogues",
author = "Nath, Abhijnan and
Venkatesha, Videep and
Bradford, Mariah and
Chelle, Avyakta and
Youngren, Austin C. and
Mabrey, Carlos and
Blanchard, Nathaniel and
Krishnaswamy, Nikhil",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.305/",
doi = "10.18653/v1/2024.findings-emnlp.305",
pages = "5297--5314",
abstract = "Question-asking in collaborative dialogue has long been established as key to knowledge construction, both in internal and collaborative problem solving. In this work, we examine probing questions in collaborative dialogues: questions that explicitly elicit responses from the speaker`s interlocutors. Specifically, we focus on modeling the causal relations that lead directly from utterances earlier in the dialogue to the emergence of the probing question. We model these relations using a novel graph-based framework of *deliberation chains*, and realize the problem of constructing such chains as a coreference-style clustering problem. Our framework jointly models probing and causal utterances and the links between them, and we evaluate on two challenging collaborative task datasets: the Weights Task and DeliData. Our results demonstrate the effectiveness of our theoretically-grounded approach compared to both baselines and stronger coreference approaches, and establish a standard of performance in this novel task."
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<abstract>Question-asking in collaborative dialogue has long been established as key to knowledge construction, both in internal and collaborative problem solving. In this work, we examine probing questions in collaborative dialogues: questions that explicitly elicit responses from the speaker‘s interlocutors. Specifically, we focus on modeling the causal relations that lead directly from utterances earlier in the dialogue to the emergence of the probing question. We model these relations using a novel graph-based framework of *deliberation chains*, and realize the problem of constructing such chains as a coreference-style clustering problem. Our framework jointly models probing and causal utterances and the links between them, and we evaluate on two challenging collaborative task datasets: the Weights Task and DeliData. Our results demonstrate the effectiveness of our theoretically-grounded approach compared to both baselines and stronger coreference approaches, and establish a standard of performance in this novel task.</abstract>
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%0 Conference Proceedings
%T “Any Other Thoughts, Hedgehog?” Linking Deliberation Chains in Collaborative Dialogues
%A Nath, Abhijnan
%A Venkatesha, Videep
%A Bradford, Mariah
%A Chelle, Avyakta
%A Youngren, Austin C.
%A Mabrey, Carlos
%A Blanchard, Nathaniel
%A Krishnaswamy, Nikhil
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F nath-etal-2024-thoughts
%X Question-asking in collaborative dialogue has long been established as key to knowledge construction, both in internal and collaborative problem solving. In this work, we examine probing questions in collaborative dialogues: questions that explicitly elicit responses from the speaker‘s interlocutors. Specifically, we focus on modeling the causal relations that lead directly from utterances earlier in the dialogue to the emergence of the probing question. We model these relations using a novel graph-based framework of *deliberation chains*, and realize the problem of constructing such chains as a coreference-style clustering problem. Our framework jointly models probing and causal utterances and the links between them, and we evaluate on two challenging collaborative task datasets: the Weights Task and DeliData. Our results demonstrate the effectiveness of our theoretically-grounded approach compared to both baselines and stronger coreference approaches, and establish a standard of performance in this novel task.
%R 10.18653/v1/2024.findings-emnlp.305
%U https://aclanthology.org/2024.findings-emnlp.305/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.305
%P 5297-5314
Markdown (Informal)
[“Any Other Thoughts, Hedgehog?” Linking Deliberation Chains in Collaborative Dialogues](https://aclanthology.org/2024.findings-emnlp.305/) (Nath et al., Findings 2024)
ACL
- Abhijnan Nath, Videep Venkatesha, Mariah Bradford, Avyakta Chelle, Austin C. Youngren, Carlos Mabrey, Nathaniel Blanchard, and Nikhil Krishnaswamy. 2024. “Any Other Thoughts, Hedgehog?” Linking Deliberation Chains in Collaborative Dialogues. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 5297–5314, Miami, Florida, USA. Association for Computational Linguistics.