Improving Dialog Systems Using Knowledge Graph Embeddings

Dialog systems are systems or applications intended to converse with a human user. Recent dialog systems have employed the sequence-to-sequence framework to treat conversation as a translation problem, translating from question to answer in an open-domain. Knowledge graph embedding started as a way to scale question answering to a large, open-domain dataset without the use of hand-crafted rules. This thesis seeks to connect the two, by converting knowledge graph embeddings to word embeddings and evaluating the resulting dialog models.