Yeoil Yoon


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A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning
Yo Joong Choe | Jiyeon Ham | Kyubyong Park | Yeoil Yoon
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

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 sub-sequently 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 BEAShared Task. We release all of our code and materials for reproducibility.