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