Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation
Table 8: Results on math problems (§5.5).
['Method', 'UF1', 'LF1']
Remarkably, we observe huge additive performance improvement. While, in terms of labeled F1, ELMo contributes about 4 points on top of the plain depccg, adding the new training set (converted from dependency trees) improves more than 10 points. Examining the resulting trees, we observe that the huge gain is primarily involved with expressions unique to math. However, after fine-tuning, it successfully produces the correct “ If S1 and S2, S3” structure, recognizing that the equal sign is a predicate.