论文标题
对会话问题回答的问题重写
Question Rewriting for Conversational Question Answering
论文作者
论文摘要
会话问题回答（QA）需要在以前的对话转弯的上下文中正确解释问题的能力。我们通过将其分解为问题重写和回答子任务来解决对话质量检查任务。重写（QR）子任务的问题是专门设计的，目的是将歧义的问题重新制定，这些问题取决于会话上下文，为明确的问题，这些问题可以在对话性上下文之外正确解释。我们介绍了一种对话质量检查架构，该体系结构在TREC Cast 2019通道检索数据集上设置了新的最新技术。此外，我们表明，相同的QR模型相对于答案跨度提取了QU数据集上的QA性能，这是通过回收后QA的下一步。我们的评估结果表明，我们提出的QR模型在数据集上达到了人类级别的性能以及端到端对话质量质量质量质量标准任务的性能差距，主要归因于质量检查中的错误。
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.
