Papers › Grammatical Error Correction in Low-Resource Scenarios

Grammatical Error Correction in Low-Resource Scenarios

1 Oct 2019WS 2019 11arXiv:1910.00353archive 2025-07-28

Jakub Náplava, Milan Straka

Grammatical error correction in English is a long studied problem with many existing systems and datasets. However, there has been only a limited research on error correction of other languages. In this paper, we present a new dataset AKCES-GEC on grammatical error correction for Czech. We then make experiments on Czech, German and Russian and show that when utilizing synthetic parallel corpus, Transformer neural machine translation model can reach new state-of-the-art results on these datasets. AKCES-GEC is published under CC BY-NC-SA 4.0 license at https://hdl.handle.net/11234/1-3057 and the source code of the GEC model is available at https://github.com/ufal/low-resource-gec-wnut2019.

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Code

ufal/low-resource-gec-wnut2019 officialmentioned in paper report

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Tasks

Grammatical Error Correction Machine Translation Translation

Datasets

Introduced by this paper, per the archive.

AKCES-GEC

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Grammatical Error Correction	Falko-MERLIN	Transformer	F0.5	73.71	#4 of 6	Archive leaderboard	report
Grammatical Error Correction	Falko-MERLIN	Transformer - synthetic pretrain only	F0.5	51.41	#5 of 6	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer ReLU Residual Connection Softmax Transformer

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