Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context. Moreover, it can be expensive to re-train well-established retrievers such as search engines that are originally developed for non-conversational queries. To facilitate their use, we develop a query rewriting model CONQRR that rewrites a conversational question in the context into a standalone question. It is trained with a novel reward function to directly optimize towards retrieval using reinforcement learning and can be adapted to any off-the-shelf retriever. We show that CONQRR achieves state-of-the-art results on a recent open-domain CQA dataset containing conversations from three different sources, and is effective for two different off-the-shelf retrievers. Our extensive analysis also shows the robustness of CONQRR to out-of-domain dialogues as well as to zero query rewriting supervision.
翻译:与标准检索任务相比,对谈话问题解答的回录(CQA)在理解当前用户问题方面提出了新的挑战,因为每个问题都需要在对话背景下加以解释。此外,再培训成熟的检索器,例如最初为非问答查询开发的搜索引擎等,可能费用高昂。为了便于使用,我们开发了查询重写模型COQRR,该模型在背景中将一个谈话问题重写为一个独立的问题。它经过新颖的奖励功能培训,以直接优化为利用强化学习检索,并可以适应任何现成检索器。我们显示,CONQRR在最近一个包含来自三个不同来源的对话的开放域 CQA数据集上取得了最新的结果,对两个不同的现成检索器有效。我们的广泛分析还显示,CONQRR对外部对话以及零回写监管的强大性。