Papers › A Neural Grammatical Error Correction System Built On Better Pre-training and...

A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning

2 Jul 2019WS 2019 8arXiv:1907.01256archive 2025-07-28

Yo Joong Choe, Jiyeon Ham, Kyubyong Park, Yeoil Yoon

Grammatical error correction can be viewed as a low-resource sequence-to-sequence task, because publicly available parallel corpora are limited. To tackle this challenge, we first generate erroneous versions of large unannotated corpora using a realistic noising function. The resulting parallel corpora are subsequently used to pre-train Transformer models. Then, by sequentially applying transfer learning, we adapt these models to the domain and style of the test set. Combined with a context-aware neural spellchecker, our system achieves competitive results in both restricted and low resource tracks in ACL 2019 BEA Shared Task. We release all of our code and materials for reproducibility.

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Code

kakaobrain/helo_word officialmentioned in papermentioned on GitHubpytorch report

kakaobrain/helo-word mentioned on GitHubpytorch report

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Tasks

Grammatical Error Correction Transfer Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Grammatical Error Correction	BEA-2019 (test)	Transformer	F0.5	69.0	#17 of 19	Archive leaderboard	report

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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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