Learning How to Self-Learn: Enhancing Self-Training Using Neural Reinforcement Learning
Table 3: Performance comparison for POS tagging (%)
['[EMPTY]', '[BOLD] Newsgroup', '[BOLD] Reviews', '[BOLD] Weblogs', '[BOLD] Emails']
Results. We can observe that our DQN-based solution has a better performance than the two baseline solutions in each target domain. In particular, Fig. The accuracy of the two baselines also increase with the increasing number of unlabeled instance, but on average the accuracy of the DQN-solution is about 0.4% and 0.6% more than the TSL and RD solutions, respectively. Similar results can be observed for the Reviews and Emails scenarios, as shown in Fig. In the Weblogs scenario, as shown in Fig. In contrast, the performance of the TSL and DQN solutions increases slowly. On average, the DQN solution has achieved an improvement of 0.04% than the TSL solution.