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One of the main challenges in conversational question answering (CQA) is toresolve the conversational dependency, such as anaphora and ellipsis. However,existing approaches do not explicitly train QA models on how to resolve thedependency, and thus these models are limited in understanding human dialogues.In this paper, we propose a novel framework, ExCorD (Explicit guidance on howto resolve Conversational Dependency) to enhance the abilities of QA models incomprehending conversational context. ExCorD first generates self-containedquestions that can be understood without the conversation history, then trainsa QA model with the pairs of original and self-contained questions using aconsistency-based regularizer. In our experiments, we demonstrate that ExCorDsignificantly improves the QA models' performance by up to 1.2 F1 on QuAC, and5.2 F1 on CANARD, while addressing the limitations of the existing approaches.

Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering