MixEI: Mixing explicit and implicit commonsense knowledge in open-domain dialogue response generation

Abstract

The inadequate awareness of real-world knowledge often causes machines to produce generic responses, such as ‘I think so.’, which may bring the degression of user interests. Consequently, enriching knowledge awareness and fabricating informative responses are long-standing challenges in open-domain dialogue systems. Previous studies have shown incorporating everyday commonsense knowledge can significantly enhance the open-domain dialogue response models. Nonetheless, previous works can only use explicit or implicit knowledge. Unlike them, this work presents a novel MixEI to leverage both explicit and implicit commonsense knowledge in dialogue generation. MixEI uses Dual-Way Knowledge Alignment and MixEI-Ranker to retrieve a set of contextually relevant commonsense facts as the explicit background knowledge and identify implicit knowledge labels by selecting clue facts that can tightly connect the dialogue context. MixEI uses BART as the backbone. After jointly encoding the background knowledge and dialogue history, MixEI first tries to externalize the implicit clue knowledge; then, the response decoding can seek information from both explicit and implicit knowledge. Extensive experiments on Chinese Weibo and English Reddit have verified the superior performance of the proposed MixEI-Ranker and MixEI.