Learning Numeral Embedding
Table 5: The results of sequence labeling. We report the accuracy, precision, recall, F1 score for the original, augmented, and harder test sets with different training data sizes. Accuracy is in the token level and the other metrics are in the entity level. We report the standard deviations in Appendix H.
['[EMPTY]', '[EMPTY]', 'Original Acc', 'Original P', 'Original R', 'Original F1', 'Augmented Acc', 'Augmented P', 'Augmented R', 'Augmented F1', 'Hard Acc', 'Hard P', 'Hard R', 'Hard F1']
Our methods consistently outperform all the baselines on the Accuracy, Recall, and F1 metrics in different configurations. NumAsTok trained with 100% training samples has the highest precision on the original and hard test sets probably because it learns high-quality embeddings for high-frequency numerals included in its vocabulary; but its recall is lower than that of our methods, most likely because of its numeral OOV problem. Comparing the results on the original and augmented test sets, we see that NumAsTok shows a more significant drop in performance than the other methods, which suggests that NumAsTok does not generalize well because of the numeral OOV problem. In the low-resource settings, the advantage of our methods over the baselines becomes even larger, indicating better generalizability and less annotation required for our methods to achieve a promising performance.