Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
This addresses ambiguity handling for users in open-domain QA, but it is incremental as it builds on prior disambiguation methods.
The paper tackles the problem of ambiguous questions in open-domain QA by proposing to ask clarification questions to identify user intent, and it achieves 61.3 F1 on ambiguity detection and 40.5 F1 on clarification-based QA.
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.