Improving Domain-Adapted Sentiment Classification byDeep Adversarial Mutual Learning
Table 2: Sentiment classification results in terms of Acc and RMSE. The best results are marked in bold.
['Source ↪ Target', 'Metrics', 'Baselines Naive', 'Baselines DANN', 'Baselines MMD', 'Baselines WDGRL', 'Baselines DAS', 'Baselines ACAN', 'Our Approaches NE', 'Our Approaches ML', 'Our Approaches FA', 'Our Approaches DAML']
Our final approach DAML outperforms all the other approaches on most tasks. Compared with the NE framework, which in average performs the best among all the left approaches, DAML improves from 63.4% to 64.5% in terms of accuracy and reduces the RMSE error from 0.925 to 0.898. Moreover, NE requires multiple available classification models when testing, while DAML only needs a single model that performs the best on the development set of a source domain.