论文标题
自我注意比较模块，用于提高基于检索的开放域对话框系统的性能
Self-attention Comparison Module for Boosting Performance on Retrieval-based Open-Domain Dialog Systems
论文作者
论文摘要
由于预先训练的语言模型被广泛使用，因此基于检索的开放域对话框系统最近引起了研究人员的广泛关注。以前的大多数作品仅根据查询与每个候选人响应之间的匹配程度选择合适的响应。尽管已经取得了良好的表现，但这些最近的作品忽略了候选人的回应之间的比较，这可以为选择最合适的响应提供丰富的信息。当模型可以访问所有候选响应之间的比较信息时，就可以做出更好的决策。为了利用候选人响应之间的比较信息，在本文中，我们为基于检索的开放域对话框系统（称为SCM）提出了一个新颖和插件的自我发言比较模块。广泛的实验结果表明，我们提出的自我注意比较模块有效地提高了现有基于检索的开放域对话框系统的性能。此外，我们已公开发布了未来研究的源代码。
Since the pre-trained language models are widely used, retrieval-based open-domain dialog systems, have attracted considerable attention from researchers recently. Most of the previous works select a suitable response only according to the matching degree between the query and each individual candidate response. Although good performance has been achieved, these recent works ignore the comparison among the candidate responses, which could provide rich information for selecting the most appropriate response. Intuitively, better decisions could be made when the models can get access to the comparison information among all the candidate responses. In order to leverage the comparison information among the candidate responses, in this paper, we propose a novel and plug-in Self-attention Comparison Module for retrieval-based open-domain dialog systems, called SCM. Extensive experiment results demonstrate that our proposed self-attention comparison module effectively boosts the performance of the existing retrieval-based open-domain dialog systems. Besides, we have publicly released our source codes for future research.
