Description Based Text Classification with Reinforcement Learning
Table 2: Test error rates on the AGNews, 20news, DBPedia, Yahoo, Yelp P and IMDB datasets for single-label classification. ‘–’ means not reported results. ‘♯’ means we take the best reported results from 10.1007/978-3-030-32381-3_16.
['[BOLD] Model', 'AGNews', '20news', 'DBPedia', 'Yahoo', 'YelpP', 'IMDB']
The three proposed strategies consistently outperform the BERT baseline. Specifically, the template-based strategy outperforms BERT, decreasing error rates by i.e., -0.7 on AGNews, -1.1 on 20news, -0.07 on DBPedia, -0.6 on Yahoo, -0.2 on YelpP and -1.0 on IMDB. The extractive and abstractive strategies consistently outperform the template-based strategy, which is because of their ability to automatically learn the proper descriptions. The extractive strategy performs better than the abstractive strategy on the AGNews and IMDB, but worse on the others.