Improving Domain-Adapted Sentiment Classification byDeep Adversarial Mutual Learning
Table 3: Results of DAML trained on different domains, with its probers exclusively applied to the domain indicated by the column name.
['Source ↪ Target', 'Metrics', 'Target Domain', 'Source Domain', 'Both Domains']
We can see training the probers of DAML on the source domain exclusively performs much worse than the ones trained on the target domain, or both domains. The comparison emphasizes the importance of leveraging the sentiment information in the target domains. According to the results, we can conclude that what makes DAML perform well is its ability of taking an advantage of pseudo-labels.