Bridging the domain gap in cross-lingual document classification
Table 8: Error rates for cross-domain document classification
['Base Model', 'Train', 'amazon-en', 'yelp', 'amazon-cn', 'dianping', 'Unlabeled']
As further evidence that our method addresses the domain mismatch, we apply out framework to the monolingual cross-domain document classification problem. We again focus on sentiment classification where data comes from two different domains, product reviews (amazon-en, amazon-cn) and business reviews (Yelp and Dianping). We train and test on the same language, only transferring across domains. We consider the two domain-pairs, amazonen-yelp and amazoncn-dianping. : [leftmargin=*] There exists a clear gap between the cross-domain and in-domain results of the Ft method, even when using strong pre-trained representations. By leveraging the unlabeled data from the target domain, we can significantly boost the model performance. Best results are achieved with our combined approach and almost completely matches the in-domain baselines.