@inproceedings{ikumariegbe-etal-2025-studying,
title = "Studying Rhetorically Ambiguous Questions",
author = "Ikumariegbe, Oghenevovwe and
Blanco, Eduardo and
Riloff, Ellen",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1553/",
pages = "30507--30517",
ISBN = "979-8-89176-332-6",
abstract = "Distinguishing between rhetorical questions and informational questions is a challenging task, as many rhetorical questions have similar surface forms to informational questions. Existing datasets, however, do not contain many questions that can be rhetorical or informational in different contexts. We introduce Studying Rhetorically Ambiguous Questions (SRAQ), a new dataset explicitly constructed to support the study of such rhetorical ambiguity. The questions in SRAQ can be interpreted as either rhetorical or informational depending on the context. We evaluate the performance of state-of-the-art language models on this dataset and find that they struggle to recognize many rhetorical questions."
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<abstract>Distinguishing between rhetorical questions and informational questions is a challenging task, as many rhetorical questions have similar surface forms to informational questions. Existing datasets, however, do not contain many questions that can be rhetorical or informational in different contexts. We introduce Studying Rhetorically Ambiguous Questions (SRAQ), a new dataset explicitly constructed to support the study of such rhetorical ambiguity. The questions in SRAQ can be interpreted as either rhetorical or informational depending on the context. We evaluate the performance of state-of-the-art language models on this dataset and find that they struggle to recognize many rhetorical questions.</abstract>
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%0 Conference Proceedings
%T Studying Rhetorically Ambiguous Questions
%A Ikumariegbe, Oghenevovwe
%A Blanco, Eduardo
%A Riloff, Ellen
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F ikumariegbe-etal-2025-studying
%X Distinguishing between rhetorical questions and informational questions is a challenging task, as many rhetorical questions have similar surface forms to informational questions. Existing datasets, however, do not contain many questions that can be rhetorical or informational in different contexts. We introduce Studying Rhetorically Ambiguous Questions (SRAQ), a new dataset explicitly constructed to support the study of such rhetorical ambiguity. The questions in SRAQ can be interpreted as either rhetorical or informational depending on the context. We evaluate the performance of state-of-the-art language models on this dataset and find that they struggle to recognize many rhetorical questions.
%U https://aclanthology.org/2025.emnlp-main.1553/
%P 30507-30517
Markdown (Informal)
[Studying Rhetorically Ambiguous Questions](https://aclanthology.org/2025.emnlp-main.1553/) (Ikumariegbe et al., EMNLP 2025)
ACL
- Oghenevovwe Ikumariegbe, Eduardo Blanco, and Ellen Riloff. 2025. Studying Rhetorically Ambiguous Questions. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 30507–30517, Suzhou, China. Association for Computational Linguistics.