Question Rewriting for Conversational Question Answering
This addresses the problem of ambiguous questions in conversational QA for researchers and practitioners, though it is incremental as it builds on existing decomposition approaches.
The paper tackles conversational question answering by decomposing it into question rewriting and answering subtasks, achieving new state-of-the-art results on the TREC CAsT 2019 dataset and improving performance on QuAC, with the QR model reaching near human-level performance.
Conversational question answering (QA) requires the ability to correctly interpret a question in the context of previous conversation turns. We address the conversational QA task by decomposing it into question rewriting and question answering subtasks. The question rewriting (QR) subtask is specifically designed to reformulate ambiguous questions, which depend on the conversational context, into unambiguous questions that can be correctly interpreted outside of the conversational context. We introduce a conversational QA architecture that sets the new state of the art on the TREC CAsT 2019 passage retrieval dataset. Moreover, we show that the same QR model improves QA performance on the QuAC dataset with respect to answer span extraction, which is the next step in QA after passage retrieval. Our evaluation results indicate that the QR model we proposed achieves near human-level performance on both datasets and the gap in performance on the end-to-end conversational QA task is attributed mostly to the errors in QA.