Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering
Neha Srikanth, Rupak Sarkar, Heran Mane, Elizabeth Aparicio, Quynh Nguyen, Rachel Rudinger, Jordan Boyd-Graber
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.
BibTeX
@inproceedings{srikanth-etal-2024-pregnant,
title = "Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering",
author = "Srikanth, Neha and
Sarkar, Rupak and
Mane, Heran and
Aparicio, Elizabeth and
Nguyen, Quynh and
Rudinger, Rachel and
Boyd-Graber, Jordan",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.403/",
doi = "10.18653/v1/2024.naacl-long.403",
pages = "7253--7268"
}