Domain Adaptive Training BERT for Response Selection
Table 1: Model comparison on Ubuntu Corpus V1.
['[BOLD] Model', '[ITALIC] R10@1', '[ITALIC] R10@2', '[ITALIC] R10@5']
We conduct experiments on two data sets, Ubuntu Corpus V1 and Advising Corpus. It is observed that BERT-VFT achieves new state-of-the-art performance and it obtains performance improvement by 5.9%, 3.4%, 0.9% on R10@k, where k={1,2,5}, respectively, compared to the previous state-of-the-art method. Focusing on R10@1 metric, the performance of a vanilla BERTbase is 0.817 and comparing our main approach, which is BERT-DPT, shows much better performance (improvement by 3.4%). In addition, we especially point out comparing BERT-VFT with deep attention matching network (DAM) Zhou et al. Domain-specific optimized BERT-VPT model outperforms with performance improvement by 8.8% compared to the general transformer based model.