DEEPCOPY: Grounded Response Generation with Hierarchical Pointer Networks

Recent advances in neural sequence-to-sequence models have led to promising results for several downstream generation-based natural language processing tasks including dialogue response generation, summarization, and machine translation. However, these models are known to have several problems, especially in the context of chit-chat based dialogue systems: they tend to generate short and dull responses that are often too generic. Furthermore, these models do not ground conversational responses on knowledge and facts, resulting in turns that are not informative and engaging for users. These indeed are the essential features that dialogue response generation models should be equipped with to serve in more realistic and useful conversational applications. Recently, several dialogue datasets accompanied with relevant external knowledge have been released to facilitate research into remedying such issues encountered by traditional models by resorting to this additional information. In this paper, we propose and experiment with a series of response generation models that aim to serve in the general scenario where in addition to the dialogue context, relevant unstructured external knowledge in the form of text is also assumed to be available for models to harness. We empirically show the effectiveness of the proposed model compared to several baselines on CONVAI2 challenge.