Dialogue response selection is an important part of Task-oriented Dialogue
Systems (TDSs); it aims to predict an appropriate response given a dialogue
context. Obtaining key information from a complex, long dialogue context is
challenging, especially when different sources of information are available,
e.g., the user's utterances, the system's responses, and results retrieved from
a knowledge base (KB). Previous work ignores the type of information source and
merges sources for response selection. However, accounting for the source type
may lead to remarkable differences in the quality of response selection. We
propose the Source-aware Recurrent Entity Network (SEntNet), which is aware of
different information sources for the response selection process. SEntNet
achieves this by employing source-specific memories to exploit differences in
the usage of words and syntactic structure from different information sources
(user, system, and KB). Experimental results show that SEntNet obtains 91.0%
accuracy on the Dialog bAbI dataset, outperforming prior work by 4.7%. On the
DSTC2 dataset, SEntNet obtains an accuracy of 41.2%, beating source unaware
recurrent entity networks by 2.4%.

该论文提出了一种基于来源感知的循环实体网络（SEntNet）的任务定向对话系统（TDS）中，用于优化对话响应选择的方法，通过使用源特定记忆，从不同信息源（用户、系统、知识库）中挖掘单词和句法结构的差异，有效提高了对话响应的准确性，实验表明在对话数据集（Dialog bAbI dataset）上，SEntNet 的准确性达到了 91.0％，优于现有工作 4.7％，而在 DSTC2 数据集上，SEntNet 的准确性达到了 41.2％，超越了源无关的循环实体网络 2.4％。