Which questions should I answer? Salience Prediction of Inquisitive Questions

Yating Wu, Ritika Rajesh Mangla, Alex Dimakis, Greg Durrett, Junyi Jessy Li


Abstract
Inquisitive questions — open-ended, curiosity-driven questions people ask as they read — are an integral part of discourse processing and comprehension. Recent work in NLP has taken advantage of question generation capabilities of LLMs to enhance a wide range of applications. But the space of inquisitive questions is vast: many questions can be evoked from a given context. So which of those should be prioritized to find answers? Linguistic theories, unfortunately, have not yet provided an answer to this question. This paper presents QSalience, a salience predictor of inquisitive questions. QSalience is instruction-tuned over our dataset of linguist-annotated salience scores of 1,766 (context, question) pairs. A question scores high on salience if answering it would greatly enhance the understanding of the text. We show that highly salient questions are empirically more likely to be answered in the same article, bridging potential questions with Questions Under Discussion. We further validate our findings by showing that answering salient questions is an indicator of summarization quality in news.
Anthology ID:
2024.emnlp-main.1114
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
19969–19987
Language:
URL:
https://aclanthology.org/2024.emnlp-main.1114
DOI:
10.18653/v1/2024.emnlp-main.1114
Bibkey:
Cite (ACL):
Yating Wu, Ritika Rajesh Mangla, Alex Dimakis, Greg Durrett, and Junyi Jessy Li. 2024. Which questions should I answer? Salience Prediction of Inquisitive Questions. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 19969–19987, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Which questions should I answer? Salience Prediction of Inquisitive Questions (Wu et al., EMNLP 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.emnlp-main.1114.pdf