  In this paper, we study the problem of employing pre-trained language models
for multi-turn response selection in retrieval-based chatbots. A new model,
named Speaker-Aware BERT (SA-BERT), is proposed in order to make the model
aware of the speaker change information, which is an important and intrinsic
property of multi-turn dialogues. Furthermore, a speaker-aware disentanglement
strategy is proposed to tackle the entangled dialogues. This strategy selects a
small number of most important utterances as the filtered context according to
the speakers' information in them. Finally, domain adaptation is performed to
incorporate the in-domain knowledge into pre-trained language models.
Experiments on five public datasets show that our proposed model outperforms
the present models on all metrics by large margins and achieves new
state-of-the-art performances for multi-turn response selection.
