Speaker Aware BERT for Multi Turn Response Selection in Retrieval Based Chatbots
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.
CIKM