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.

• Machine Intelligence