Approaching Neural Grammatical Error Correctionas a Low-Resource Machine Translation Task
Table 5: Results for model type +Tied-Emb. trained with edit-weighted MLE and chosen Λ.
['Λ', 'CoNLL Dev', 'CoNLL Prec.', 'CoNLL Rec.', 'CoNLL Test', 'JFLEG Dev', 'JFLEG Test']
This is comparable to reinforcement learning towards GLEU as introduced by \newcitesakaguchi2017grammatical or training against diffs by \newciteDBLP:conf/emnlp/SchmaltzKRS17. In combination with previous modifications, edit-weighted Maximum Likelihood Estimation (MLE) weighting seem to outperform both methods. The parameter Λ introduces an additional hyper-parameter that requires tuning for specific tasks and affects the precision/recall trade-off.