  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.
