  Retrieval-based dialogue systems select the best response from many
candidates. Although many state-of-the-art models have shown promising
performance in dialogue response selection tasks, there is still quite a gap
between R@1 and R@10 performance. To address this, we propose to leverage
linguistic coordination (a phenomenon that individuals tend to develop similar
linguistic behaviors in conversation) to rerank the N-best candidates produced
by BERT, a state-of-the-art pre-trained language model. Our results show an
improvement in R@1 compared to BERT baselines, demonstrating the utility of
repairing machine-generated outputs by leveraging a linguistic theory.
