Learning How to Self-Learn: Enhancing Self-Training Using Neural Reinforcement Learning
Table 1: Performance comparison for NER tagging (%)
['[EMPTY]', '[BOLD] English', '[BOLD] Dutch', '[BOLD] Spanish', '[BOLD] German']
Results. We can observe that our DQN-based solution has a better performance than the two baseline solutions. In particular, Fig. The F1-score of TSL also increases, but the F1-score of the two baselines drop dramatically after adding more unlabeled sentences. Note that although the TSL approach has a sharp increase of performance at the moment of adding 50 unlabeled sentences, it is not stable compared to our DQN-based solution. We looked into the sentences selected by each solution, and found that TSL selects almost the sentences with no entities (around 95.6% of the selected sentences), because the sentences with no entities have a high confidence value. In contrast, DQN favors the sentences with more and diverse entities, occurring in around 54.1% of the selected sentences. We can observe similar results for the Spanish and German NER tagging, as shown in Fig. In general, our DQN-based approach has a better tagging performance (about 0.3% ∼ 1% improvement on average) and is more stable than the other two baseline solutions. A complete comparison of the performance can be found in Table.