论文标题
问题重写？评估其对会话问题回答的重要性
Question rewriting? Assessing its importance for conversational question answering
论文作者
论文摘要
在会话问题的回答中，系统必须正确解释相互联系的交互并生成知识渊博的答案，这可能需要从背景存储库中检索相关信息。该问题的最新方法利用神经语言模型，尽管可以根据（a）在上下文中代表用户问题的模块来考虑不同的替代方案，（b）检索相关的背景信息，以及（c）生成答案。这项工作提出了专门为面向搜索的对话AI（SCAI）共享任务设计的对话问题答案系统，并报告了对其问题重写模块的详细分析。特别是，我们考虑了问题重写模块的不同变化，以评估对后续组件的影响，并对最佳系统配置获得的结果进行了仔细的分析。我们的系统在共享任务中实现了最佳性能，我们的分析强调了对话上下文表示对整体系统性能的重要性。
In conversational question answering, systems must correctly interpret the interconnected interactions and generate knowledgeable answers, which may require the retrieval of relevant information from a background repository. Recent approaches to this problem leverage neural language models, although different alternatives can be considered in terms of modules for (a) representing user questions in context, (b) retrieving the relevant background information, and (c) generating the answer. This work presents a conversational question answering system designed specifically for the Search-Oriented Conversational AI (SCAI) shared task, and reports on a detailed analysis of its question rewriting module. In particular, we considered different variations of the question rewriting module to evaluate the influence on the subsequent components, and performed a careful analysis of the results obtained with the best system configuration. Our system achieved the best performance in the shared task and our analysis emphasizes the importance of the conversation context representation for the overall system performance.
