  In conversational QA, models have to leverage information in previous turns
to answer upcoming questions. Current approaches, such as Question Rewriting,
struggle to extract relevant information as the conversation unwinds. We
introduce the Common Ground (CG), an approach to accumulate conversational
information as it emerges and select the relevant information at every turn. We
show that CG offers a more efficient and human-like way to exploit
conversational information compared to existing approaches, leading to
improvements on Open Domain Conversational QA.
