Asking Clarification Questions to Handle Ambiguity in Open-Domain QA

TL;DR

This paper introduces a new approach for open-domain QA that involves asking clarification questions to resolve ambiguity, supported by a new dataset and evaluation metrics, improving ambiguity detection and clarification-based answering.

Contribution

The paper presents CAMBIGNQ, a dataset of ambiguous questions with clarification queries, and establishes baseline methods for ambiguity detection and clarification-based QA.

Findings

Achieved 61.3 F1 on ambiguity detection

Achieved 40.5 F1 on clarification-based QA

Proposed effective evaluation pipeline for clarification questions

Abstract

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.