Description Based Text Classification with Reinforcement Learning
Table 4: Test error rates on the BeerAdvocate (Beer), TripAdvisor (Trip) for multi-aspect sentiment classification.
['[BOLD] Model', 'Beer', 'Trip']
Surprisingly huge gains are observed on both datasets. Specifically, for BeerAdvocate, our method (Abs.) decreases the error rate from 27.8 to 15.6, and for TripAdvisor, our method (Ext.) decreases the error rate from 35.6 to 17.0. The explanation for this huge boost is as follows: both datasets are deliberately constructed in a way that each review contains aspects with opposite sentiments entangling with each other. This makes it extremely hard for the model to learn to jointly identify the target aspect and the sentiment. The incorporation of description gives the model the ability to directly attend to the relevant text, which leads to significant performance boost.