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Despite showing increasingly human-like conversational abilities,state-of-the-art dialogue models often suffer from factual incorrectness andhallucination of knowledge (Roller et al., 2020). In this work we explore theuse of neural-retrieval-in-the-loop architectures - recently shown to beeffective in open-domain QA (Lewis et al., 2020b; Izacard and Grave, 2020) -for knowledge-grounded dialogue, a task that is arguably more challenging as itrequires querying based on complex multi-turn dialogue context and generatingconversationally coherent responses. We study various types of architectureswith multiple components - retrievers, rankers, and encoder-decoders - with thegoal of maximizing knowledgeability while retaining conversational ability. Wedemonstrate that our best models obtain state-of-the-art performance on twoknowledge-grounded conversational tasks. The models exhibit open-domainconversational capabilities, generalize effectively to scenarios not within thetraining data, and, as verified by human evaluations, substantially reduce thewell-known problem of knowledge hallucination in state-of-the-art chatbots.

Retrieval Augmentation Reduces Hallucination in Conversation