Recovering Dropped Pronouns in Chinese Conversations via Modeling Their Referents
Table 1: Results in terms of precision, recall and F-score on 16 types of pronouns produced by the baseline systems and variants of our proposed NDPR model. For NRM∗ Zhang et al. (2016), we implement the proposed model as described in the paper.
['Model', 'Chinese SMS P(%)', 'Chinese SMS R(%)', 'Chinese SMS F', 'TC of OntoNotes P(%)', 'TC of OntoNotes R(%)', 'TC of OntoNotes F', 'BaiduZhidao P(%)', 'BaiduZhidao R(%)', 'BaiduZhidao F']
We can see that our proposed model and its variants outperform the baseline methods on all these datasets by different margins. Our best model, NDPR, outperforms MEPR by 7.63% in terms of F-score on the Chinese SMS dataset, and outperforms NRM by 16.97% and 8.40% on the OntoNotes and BaiduZhidao datasets respectively. Compared with the degenerate variant model BiGRU, our NDPR model also performs better on all three datasets, which demonstrate the effectiveness of referent modeling mechanism composed of sentence-level and word-level attention. We attribute this to the fact that there are only concrete pronouns in this dataset. The combination of “我(I)”, “你(singular you)” and “它(it)” accounts for 94.47% of the overall dropped pronoun population, for which the referent can be easily determined by word-level attention. Moreover, the fewer conversation turns in this data set mean there are few irrelevant referents that need to be filtered out by sentence-level attention.