Neural Recovery Machine for Chinese Dropped Pronoun
Table 6: Experimental results on dropped pronoun recovery. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test).
['[BOLD] Models', '[BOLD] Accuracy  [BOLD] DPI', '[BOLD] Accuracy  [BOLD] DPI', '[BOLD] Accuracy  [BOLD] DPG', '[BOLD] Accuracy  [BOLD] DPG']
We compare the performance of the proposed NRM and the baselines in both of the OntoNotes 4.0 and Baidu Zhidao datasets. the Baidu Zhidao dataset for DPG task. To compare the results of the SVMSL and SVMDL, we can see that the dense representation of the dropped hypothesis has better impact on the fine grained task, namely DPG rather than the DPI task. To see the results on the two datasets, we can see that the results on Baidu Zhidao dataset are better than those on OntoNotes 4.0 dataset. It may be caused by the different scales of the two datasets and the different category numbers. We also note that the SOTA, which is a feature engineering approach, performs good when the data scale is small, such as the experiment results of DPG on OntoNotes 4.0 dataset. It may illustrates that the feature engineering approach can fit the data better when the data scale is small. However, the feature engineering is a highly empirical process and also a complicated task.