  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). Experimental results show that our approach achieves
new state-of-the-art on two response selection benchmarks (i.e., Ubuntu Corpus
V1, Advising Corpus) performance improvement by 5.9% and 6% on R@1.
