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Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering
[article]
2019
arXiv
pre-print
Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation. In addition, we propose a response guiding
arXiv:1912.07491v1
fatcat:p2cxea2ay5alpfd6kvccgamjsy