2025
EMNLP
EMNLP 2025
Studying Rhetorically Ambiguous Questions
Abstract
AbstractDistinguishing 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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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Keyword Pioneer
— informational question
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Natural Language Processing > Understanding > Semantic Analysis
Natural Language Processing > Applications > Text Classification
Natural Language Processing > Applications > Natural Language Inference
Artificial Intelligence > Core AI > Natural Language Processing
Natural Language Processing > Understanding > Semantics