@inproceedings{wu-etal-2024-questions,
title = "Which questions should {I} answer? Salience Prediction of Inquisitive Questions",
author = "Wu, Yating and
Mangla, Ritika Rajesh and
Dimakis, Alex and
Durrett, Greg and
Li, Junyi Jessy",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1114",
doi = "10.18653/v1/2024.emnlp-main.1114",
pages = "19969--19987",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Which questions should I answer? Salience Prediction of Inquisitive Questions
%A Wu, Yating
%A Mangla, Ritika Rajesh
%A Dimakis, Alex
%A Durrett, Greg
%A Li, Junyi Jessy
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wu-etal-2024-questions
%X 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.
%R 10.18653/v1/2024.emnlp-main.1114
%U https://aclanthology.org/2024.emnlp-main.1114
%U https://doi.org/10.18653/v1/2024.emnlp-main.1114
%P 19969-19987
Markdown (Informal)
[Which questions should I answer? Salience Prediction of Inquisitive Questions](https://aclanthology.org/2024.emnlp-main.1114) (Wu et al., EMNLP 2024)
ACL