Cross-lingual Pseudo-Projected Expectation Regularization for Weakly Supervised Learning
Table 1: Chinese and German NER results on the development set using CLiPPER with varying amounts of unlabeled bitext (10k, 20k, etc.). Best number of each column is highlighted in bold. The F1 score improvements over CRF baseline in all cases are statistically significant at 99.9% confidence level.
['[EMPTY]', '[EMPTY]', 'Chinese P', 'Chinese R', 'Chinese F1', 'German P', 'German R', 'German F1']
In the semi-supervised experiments, we let the CRF model use the full set of labeled examples in addition to the unlabeled bitext. We see that with merely 10k additional bitext, CLiPPER is able to improve significantly over state-of-the-art CRF baselines by as much as 1.5% F1 on both Chinese and German. With more unlabeled data, we notice a tradeoff between precision and recall on Chinese. The final F1 score on Chinese at 80k level is only marginally better than 10k. On the other hand, we observe a modest but steady improvement on German as we add more unlabeled bitext, up until 40k sentences. We select the best configurations on development set (80k for Chinese and 40k for German) to evaluate on test set.