Multinomial Adversarial Networks for Multi-Domain Text Classification
Table 3: Results on the FDU-MTL dataset. Bolded models are ours, while the rest is from P17-1001. Highest performance is each domain is highlighted. For our full MAN models, standard errors are shown in parenthese and statistical significance (p<0.01) over ASP-MTL is indicated by *.
['[EMPTY]', 'books', 'elec.', 'dvd', 'kitchen', 'apparel', 'camera', 'health', 'music', 'toys', 'video', 'baby', 'magaz.', 'softw.', 'sports', 'IMDb', 'MR', 'Avg.']
When MAN is introduced, we attain the state-of-the-art performance on every domain with a 88.4% overall accuracy, surpassing ASP-MTL by a significant margin of 2.3%.