Building dialogue systems that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. Dialogue system is categorized into task-oriented system and open-domain system. In open-domain dialogue system, the system is expected to respond to human utterances in an interesting and engaging way. With the development of deep learning and the availability of large corpora, there is a trend towards developing generative dialogue systems. However, current generative dialogue system is learned directly from the dialogue corpus and ignores the common sense knowledge, which makes it difficult to respond substantively. Hence, common sense knowledge has to be effectively integrated in the generative dialogue system. Knowledge graph is a structured...[ Read more ]

Building dialogue systems that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. Dialogue system is categorized into task-oriented system and open-domain system. In open-domain dialogue system, the system is expected to respond to human utterances in an interesting and engaging way. With the development of deep learning and the availability of large corpora, there is a trend towards developing generative dialogue systems. However, current generative dialogue system is learned directly from the dialogue corpus and ignores the common sense knowledge, which makes it difficult to respond substantively. Hence, common sense knowledge has to be effectively integrated in the generative dialogue system. Knowledge graph is a structured data to represent the common sense knowledge. In this thesis, we investigate the impact of connecting knowledge graph with the generative dialogue system. In single-turn setting, we propose generative dialogue system (GenDS) which can generate the reply with multiple knowledge triples. Besides, GenDS does not rely on the representations of entities, thus can handle out-of-vocabulary entities. In the multi-turn scenario, we propose a multi-turn GenDS which jointly takes into account message history and related common sense for generating a coherent response. We collect a human-to-human conversation data (ConversMusic) with knowledge annotations. The proposed systems are evaluated on CoversMusic and a public question answering dataset. Our proposed systems outperform baseline methods significantly in terms of the BLEU, entity accuracy, entity recall and human evaluation.