We focus on multi-turn response selection in a retrieval-based dialog system. In this paper, we utilize the powerful pre-trained language model Bi-directional Encoder Representations from Transformer (BERT) for a multi-turn dialog system and propose a highly effective post-training method on domain-specific corpus. Although BERT is easily adopted to various NLP tasks and outperforms previous baselines of each task, it still has limitations if a task corpus is too focused on a certain domain. Post-training on domain-specific corpus (e.g., Ubuntu Corpus) helps the model to train contextualized representations and words that do not appear in general corpus (e.g., English Wikipedia). Experiment results show that our approach achieves new state-of-thearts on two response selection benchmark data sets (i.e., Ubuntu Corpus V1, Advising Corpus) performance improvement by 5.9% and 6% on R 10 @1.
Recently, domain adaptation has also been used for multi-turn response selection in a retrievalbased dialog system by REF .
Domain Adaptive Training BERT for Response Selection
ArXiv
ArXiv
Computer Science