Papers › Unsupervised Grammatical Error Correction Rivaling Supervised Methods

Unsupervised Grammatical Error Correction Rivaling Supervised Methods

6 Dec 2023EMNLP 2023 2023 12archive 2025-07-28

Hannan Cao, Liping Yuan, Yuchen Zhang, Hwee Tou Ng

State-of-the-art grammatical error correction (GEC) systems rely on parallel training data (ungrammatical sentences and their manually corrected counterparts), which are expensive to construct. In this paper, we employ the Break-It-Fix-It (BIFI) method to build an unsupervised GEC system. The BIFI framework generates parallel data from unlabeled text using a fixer to transform ungrammatical sentences into grammatical ones, and a critic to predict sentence grammaticality. We present an unsupervised approach to build the fixer and the critic, and an algorithm that allows them to iteratively improve each other. We evaluate our unsupervised GEC system on English and Chinese GEC. Empirical results show that our GEC system outperforms previous unsupervised GEC systems, and achieves performance comparable to supervised GEC systems without ensemble. Furthermore, when combined with labeled training data, our system achieves new state-of-the-art results on the CoNLL-2014 and NLPCC-2018 test sets.

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Grammatical Error CorrectionSentence

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Grammatical Error Correction BEA-2019 (test) Unsupervised GEC + cLang8 F0.5 76.5 #6 of 19 Archive leaderboard report
Grammatical Error Correction CoNLL-2014 Shared Task Unsupervised GEC + cLang8 F0.5 69.6 #5 of 23 Archive leaderboard report
Grammatical Error Correction CoNLL-2014 Shared Task Unsupervised GEC + cLang8 Precision 75.0 #5 of 23 Archive leaderboard report
Grammatical Error Correction CoNLL-2014 Shared Task Unsupervised GEC + cLang8 Recall 53.8 #5 of 23 Archive leaderboard report

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