Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification
Table 3. [Color online] Accuracy and F1 scores with BERT as embeddings. Here, each target domain represents a 15% held-out data. Blue and red color represent the best and second best values for a given target domain. AWLD, IWLD and YWLD represent training on weakly labeled datasets only. ACAD-AWLD, IMDB-IWLD and YELP-YWLD represents our two-stage training procedure.
['[EMPTY]', '[EMPTY]', 'Target Domains ACAD', 'Target Domains ACAD', 'Target Domains WEAT', 'Target Domains WEAT', 'Target Domains ADLD', 'Target Domains ADLD', 'Target Domains IMDB', 'Target Domains IMDB', 'Target Domains YELP', 'Target Domains YELP', 'Target Domains SCCD', 'Target Domains SCCD']
BERT performed significantly better than ELMO. Our proposed training procedure performed exceptionally well, in some cases even at par with the models that are trained and tested on the same domain. This training procedure not only improves against standard lift-and-shift models but also leads to higher transfer results. Again, training only on WLD datasets produced the worst results. However, the values are better than the corresponding ELMO-based models. Even though, YELP-YWLD performs marginally poor ( 0.3% lower than YELP), it performs significantly better in other domains such as ADLD (6% higher than YELP) and IMDB (4.7% higher than YELP). Similar transfer improvements are reported for ACAD-AWLD and IMDB-IWLD. This transfer performance improvement reconfirms the usefulness of two-stage training procedure. As expected, domain transfer from SCCD and WEAT is inferior due to high dissimilarity between SCCD and WEAT and other domains.