Grayscale Data Construction and Multi-Level Ranking Objective for Dialogue Response Selection
Table 3: Experimental results of matching models trained with the combination strategy of our approach and the co-teaching framework. We report the results of SMN+CoT and DAM+CoT from feng2019learning on Douban and we supplement the results of two models trained with the co-teaching framework on Ubuntu. We initialize the weights of the SMN/DAM+LX+CoT from the models trained with LX objectives.
['Model', 'Douban MAP', 'Douban MRR', 'Douban P@1', 'Douban R10@1', 'Douban R10@2', 'Douban R10@5', 'Ubuntu R2@1', 'Ubuntu R10@1', 'Ubuntu R10@2', 'Ubuntu R10@5']
As we can see, models trained with our approach outperform those trained with the co-teaching framework. More importantly, the SMN and DAM trained by LS+CoT have further improvements after adding our multi-level ranking objective LS. This demonstrates that our approach is compatible with the co-teaching framework and shows strong portability and practicability to act as a generalized approach.