Adversarial Training Based Multi-Source Unsupervised Domain Adaptationfor Sentiment Analysis
Table 3: Results on FDU-MTL dataset. The highest performance in each domain is highlighted. The numbers in brackets represent the improvements relative to the Meta baseline. Results of ’MAN’ are obtained by setting private features to zeros and other results are taken from their original papers.
['Target', 'books', 'elec.', 'dvd', 'kitchen', 'apparel', 'camera', 'health', 'music', 'toys', 'video', 'baby', 'magaz.', 'sofw.', 'sports', 'IMDb', 'MR', 'Avg']
In average accuracy, we obtain 1.2 and 1.5 percent improvement for our two introduced frameworks separately.