Frustratingly Easy System Combination for Grammatical Error Correction
Muhammad Qorib, Seung‐Hoon Na, Hwee Tou Ng · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
In this paper, we formulate system combination for grammatical error correction (GEC) as a simple machine learning task: binary classification.We demonstrate that with the right problem formulation, a simple logistic regression algorithm can be highly effective for combining GEC models.Our method successfully increases the F 0.5 score from the highest base GEC system by 4.2 points on the CoNLL-2014 test set and 7.2 points on the BEA-2019 test set.Furthermore, our method outperforms the state of the art by 4.0 points on the BEA-2019 test set, 1.2 points on the CoNLL-2014 test set with original annotation, and 3.4 points on the CoNLL-2014 test set with alternative annotation.We also show that our system combination generates better corrections with higher F 0.5 scores than the conventional ensemble.1