论文标题
加强问题重写以对话问题回答
Reinforced Question Rewriting for Conversational Question Answering
论文作者
论文摘要
会话问题回答（CQA）旨在回答对话中包含的问题，如果没有上下文，这些问题是不容易解释的。开发一个模型以将对话问题重写为独立的问题是行业环境中的新兴解决方案，因为它允许使用现有的单转QA系统来避免从头开始训练CQA模型。以前的工作使用人类重写作为监督训练改写模型。但是，此类目标与质量检查模型断开了连接，因此更类似人类的重写并不能保证更好的质量保证效果。在本文中，我们建议使用QA反馈通过增强学习来监督重写模型。实验表明，我们的方法可以有效地改善质量检查的性能，而不是基准的提取性和检索QA。此外，人类评估表明，与人类注释相比，我们的方法可以产生更准确和详细的重写。
Conversational Question Answering (CQA) aims to answer questions contained within dialogues, which are not easily interpretable without context. Developing a model to rewrite conversational questions into self-contained ones is an emerging solution in industry settings as it allows using existing single-turn QA systems to avoid training a CQA model from scratch. Previous work trains rewriting models using human rewrites as supervision. However, such objectives are disconnected with QA models and therefore more human-like rewrites do not guarantee better QA performance. In this paper we propose using QA feedback to supervise the rewriting model with reinforcement learning. Experiments show that our approach can effectively improve QA performance over baselines for both extractive and retrieval QA. Furthermore, human evaluation shows that our method can generate more accurate and detailed rewrites when compared to human annotations.
