What’s in a Domain? Learning Domain-Robust Text Representations using Adversarial Training
Table 3: Accuracy [%] of different models over 4 domains (B, D, E and K) under out-of-domain evaluations on Multi Domain Sentiment Dataset. Key: ♣ from blitzer2007biographies; ♢ from ganin2015unsupervised.
['Models', 'B', 'D', 'E', 'K']
Overall, our proposed methods consistently outperform the baselines, with the Gen +d+g approach a consistent winner over all other techniques. For comparison, we also report the best results of SCL-MI and DANN, in both cases using an oracle selection of source domain. Our method consistently outperform these approaches, despite having no test oracle, although note that we use more diverse data sources for training.