DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs

Data-driven, knowledge-grounded neural conversation models are capable of generating more informative responses. However, these models have not yet demonstrated that they can zero-shot adapt to updated, unseen knowledge graphs. This paper proposes a new task about how to apply dynamic knowledge graphs in neural conversation model and presents a novel TV series conversation corpus (DyKgChat) for the task. Also, we propose a preliminary model that selects an output from two networks at each time step: a sequence-to-sequence model (Seq2Seq) and a multi-hop reasoning model, in order to support dynamic knowledge graphs. To benchmark this new task and evaluate the capability of adaptation, we introduce several evaluation metrics and the experiments show that our proposed approach outperforms previous knowledge-grounded conversation models. The proposed corpus and model can motivate the future research directions 1 . 1 Introduction In the chitchat dialogue generation, neural conversation models (Sutskever et al., 2014; Sordoni et al., 2015; Vinyals and Le, 2015) have emerged for its capability to be fully data-driven and end-to-end trained. While the generated responses are often reasonable but general (without useful information), recent work proposed knowledge-grounded models (Eric et al., 2017; Ghazvinine-jad et al., 2018; Zhou et al., 2018b; Qian et al., 2018) to incorporate external facts in an end-to- end fashion without handcrafted slot filling. Effectively combining text and external knowledge1 The data and code are available in https://github. Nonetheless, prior work rarely analyzed the model capability of zero-shot adaptation to dynamic knowledge graphs, where the states/entities and their relations are temporal and evolve as a single time scale process.