Young, Tom, Cambria, Erik, Chaturvedi, Iti, Zhou, Hao, Biswas, Subham, and Huang, Minlie (2018) Augmenting end-to-end dialogue systems with commonsense knowledge. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. pp. 4970-4977. From: AAAI-18: 32nd AAAI Conference on Artificial Intelligence, 2-7 February 2018, New Orleans, LA, USA.

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Abstract

Building dialogue systems that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. In open-domain human-computer conversation, where the conversational agent is expected to respond to human utterances in an interesting and engaging way, commonsense knowledge has to be integrated into the model effectively. In this paper, we investigate the impact of providing commonsense knowledge about the concepts covered in the dialogue. Our model represents the first attempt to integrating a large commonsense knowledge base into end-to-end conversational models. In the retrieval-based scenario, we propose a model to jointly take into account message content and related commonsense for selecting an appropriate response. Our experiments suggest that the knowledge-augmented models are superior to their knowledge-free counterparts.

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