External Knowledge Acquisition for End-to-End Document-Oriented Dialog Systems
Tuan Lai, Giuseppe Castellucci, Saar Kuzi, Heng Ji, Oleg Rokhlenko · 2023
End-to-end neural models for conversational AI often assume that a response can be generated by considering only the knowledge acquired by the model during training.Documentoriented conversational models make a similar assumption by conditioning the input on the document and assuming that any other knowledge is captured in the model's weights.However, a conversation may refer to external knowledge sources.In this work, we present EKo-DoC, an architecture for documentoriented conversations with access to external knowledge: we assume that a conversation is centered around a topic document and that external knowledge is needed to produce responses.EKo-DoC includes a dense passage retriever, a re-ranker, and a response generation model.We train the model end-to-end by using silver labels for the retrieval and re-ranking components that we automatically acquire from the attention signals of the response generation model.We demonstrate with automatic and human evaluations that incorporating external knowledge improves response generation in document-oriented conversations.Our architecture achieves new state-of-the-art results on the Wizard of Wikipedia dataset, outperforming a competitive baseline by 10.3% in Recall@1 and 7.4% in ROUGE-L.