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Dialogue response selection is an important part of Task-oriented DialogueSystems (TDSs); it aims to predict an appropriate response given a dialoguecontext. Obtaining key information from a complex, long dialogue context ischallenging, especially when different sources of information are available,e.g., the user's utterances, the system's responses, and results retrieved froma knowledge base (KB). Previous work ignores the type of information source andmerges sources for response selection. However, accounting for the source typemay lead to remarkable differences in the quality of response selection. Wepropose the Source-aware Recurrent Entity Network (SEntNet), which is aware ofdifferent information sources for the response selection process. SEntNetachieves this by employing source-specific memories to exploit differences inthe 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 theDSTC2 dataset, SEntNet obtains an accuracy of 41.2%, beating source unawarerecurrent entity networks by 2.4%.

SEntNet: Source-aware Recurrent Entity Network for Dialogue Response Selection