Adapting Sequence Models for Sentence Correction
Table 1: AESW development/test set correction results. GLEU and M2 differences on test are statistically significant via paired bootstrap resampling (Koehn, 2004; Graham et al., 2014) at the 0.05 level, resampling the full set 50 times.
['Model', 'GLEU Dev', 'GLEU Test', '[ITALIC] M2 Dev', '[ITALIC] M2 Test']
The Char+Bi+dom model is stronger than the Word+Bi+dom and CharCNN+dom models by 2.9 M2 (0.2 GLEU) and 3.3 M2 (0.3 GLEU), respectively. We find that training with target diffs is beneficial across all models, with an increase of about 5 M2 points for the Word+bi model, for example. Adding +dom information slightly improves effectiveness across models.