Title: Augmenting End-to-End Dialog Systems with Commonsense Knowledge,
Abstract: Building dialog agents 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 responses 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 dialog. 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 the Tri-LSTM model to jointly take into
account message and commonsense for selecting an appropriate response. Our
experiments suggest that the knowledge-augmented models are superior to their
knowledge-free counterparts in automatic evaluation.