Learning to combine Grammatical Error Corrections
Table 9: Combining fares better compared to ensemble.
['System', 'P', 'R', '[ITALIC] F0.5']
Ensemble VS Combining models results. Nematus has average ensembling built-in which enables inference over several RNN models by performing geometric average of the individual models’ probability distributions. It is also important to note that while average ensemble improves precision, it reduces recall. Combination is balancing precision and recall, improving both, in a way that maximizes F0.5. The last observation is far from trivial as most ways to combine systems would emphasize one or the other, e.g., piping would support mainly recall perhaps reducing precision. Lastly, combining is based on the types of errors and is linguistically motivated, and hence could be further improved by smart categorization and perhaps improvements of automatic detection Bryant et al.