  Ambiguous questions persist in open-domain question answering, because
formulating a precise question with a unique answer is often challenging.
Previously, Min et al. (2020) have tackled this issue by generating
disambiguated questions for all possible interpretations of the ambiguous
question. This can be effective, but not ideal for providing an answer to the
user. Instead, we propose to ask a clarification question, where the user's
response will help identify the interpretation that best aligns with the user's
intention. We first present CAMBIGNQ, a dataset consisting of 5,654 ambiguous
questions, each with relevant passages, possible answers, and a clarification
question. The clarification questions were efficiently created by generating
them using InstructGPT and manually revising them as necessary. We then define
a pipeline of tasks and design appropriate evaluation metrics. Lastly, we
achieve 61.3 F1 on ambiguity detection and 40.5 F1 on clarification-based QA,
providing strong baselines for future work.
