Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering
November 16, 2023 ยท Declared Dead ยท ๐ North American Chapter of the Association for Computational Linguistics
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Authors
Neha Srikanth, Rupak Sarkar, Heran Mane, Elizabeth M. Aparicio, Quynh C. Nguyen, Rachel Rudinger, Jordan Boyd-Graber
arXiv ID
2311.09542
Category
cs.CL: Computation & Language
Citations
9
Venue
North American Chapter of the Association for Computational Linguistics
Last Checked
4 months ago
Abstract
Questions posed by information-seeking users often contain implicit false or potentially harmful assumptions. In a high-risk domain such as maternal and infant health, a question-answering system must recognize these pragmatic constraints and go beyond simply answering user questions, examining them in context to respond helpfully. To achieve this, we study assumptions and implications, or pragmatic inferences, made when mothers ask questions about pregnancy and infant care by collecting a dataset of 2,727 inferences from 500 questions across three diverse sources. We study how health experts naturally address these inferences when writing answers, and illustrate that informing existing QA pipelines with pragmatic inferences produces responses that are more complete, mitigating the propagation of harmful beliefs.
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