Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution
Table 4: Performance comparison on whether using the proposed unknown words processing.
['[EMPTY]', 'F-score']
∙ Effect of UNK processing As we have mentioned in the previous section, traditional unknown word replacing methods are vulnerable to the real word test. To alleviate this issue, we proposed the UNK processing mechanism to recover the UNK tokens to the real words. As we can see that, by applying our UNK processing mechanism, the model do learned the positional features in these low-frequency words, and brings over 3% improvements in F-score, which indicated the effectiveness of our UNK processing approach.