  We study the problem of coarse-grained response selection in retrieval-based
dialogue systems. The problem is equally important with fine-grained response
selection, but is less explored in existing literature. In this paper, we
propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained
response selection in open-domain conversations. In our CFC model, dense
representations of query, candidate response and corresponding context is
learned based on the multi-tower architecture, and more expressive knowledge
learned from the one-tower architecture (fine-grained) is distilled into the
multi-tower architecture (coarse-grained) to enhance the performance of the
retriever. To evaluate the performance of our proposed model, we construct two
new datasets based on the Reddit comments dump and Twitter corpus. Extensive
experimental results on the two datasets show that the proposed methods achieve
a significant improvement over all evaluation metrics compared with traditional
baseline methods.
