Deep Reinforcement Learning for Chinese Zero Pronoun Resolution
Table 3: Experiment results on the test data. The first six columns show the results on different source of documents and the last column is the overall results.
['[EMPTY]', 'NW (84)', 'MZ (162)', 'WB (284)', 'BN (390)', 'BC (510)', 'TC (283)', '[BOLD] Overall']
Our reinforcement learning model surpasses all previous baselines. More specifically, for the “Overall” results, our model obtains a considerable improvement by 2.3% in F-score over the best baseline Yin et al. Moreover, we run experiments in different sources of documents and report the results for each source. The number following a source’s name indicates the amount of anaphoric zero pronoun in that source. Our model beats the best baseline in four of six sources, demonstrating the efficiency of our reinforcement learning model. The improvement gained over the best baseline in source “BC” is 4.3% in F-score, which is encouraging since it contains the most anaphoric zero pronoun. In all words, all these suggest that our model surpasses existed baselines, which demonstrates the efficiency of the proposed technique.