【摘要】 基于搜索引擎的知识对话系统需要解决三个问题：何时检索(When),检索什么(What),如何将知识与对话历史融合(How)。该文将基于搜索引擎的知识对话系统拆解为三个阶段：对话模式选择，搜索词生成以及对话回复生成，并对对话模式选择和对话回复生成两个阶段进行优化：使用置信度校准的方式降低分类结果中假阴性样本的比例，提高对话模式判断的准确率并改善搜索词生成的质量；使用控制编码的方式对生成模型进行约束以提高模型生成回复时的知识利用率，并构建排序器对对话回复做进一步的筛选优化。实验表明，该文的方法对比基线模型有较大的效果提升。在2022年语言与智能技术竞赛的知识对话任务中，该知识对话系统获得第四名的成绩。更多还原

【Abstract】 Internet-based dialogue systems need to solve three problems: when to retrieve, what to retrieve and how to integrate dialogue history and external knowledge. In this paper, we split Internet-based dialogue systems into three stages, which are dialogue mode selection, query generation and response generation. Focusing on dialogue mode selection stage and response generation stage, we propose to use confidence calibration method to reduce false negative samples after mode classification. We also constrain model by control code to improve knowledge utilization for response generation. Finally, we propose two re-rankers to improve the dialogue generation performance. The experiments show that our method can exceed baseline models, and rank fourth in the knowledge grounded dialogue track of the 2022 Language and Intelligence Challenge.更多还原