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We study a symmetric collaborative dialogue setting in which two agents, eachwith private knowledge, must strategically communicate to achieve a commongoal. The open-ended dialogue state in this setting poses new challenges forexisting dialogue systems. We collected a dataset of 11K human-human dialogues,which exhibits interesting lexical, semantic, and strategic elements. To modelboth structured knowledge and unstructured language, we propose a neural modelwith dynamic knowledge graph embeddings that evolve as the dialogue progresses.Automatic and human evaluations show that our model is both more effective atachieving the goal and more human-like than baseline neural and rule-basedmodels.

Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings