GECToR – Grammatical Error Correction: Tag, Not Rewrite
Kostiantyn Omelianchuk, Vitaliy Atrasevych, Artem Chernodub, Oleksandr Skurzhanskyi · 2020
In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder.Our system is pre-trained on synthetic data and then fine-tuned in two stages: first on errorful corpora, and second on a combination of errorful and error-free parallel corpora.We design custom token-level transformations to map input tokens to target corrections.Our best single-model/ensemble GEC tagger achieves an F 0.5 of 65.3/66.5 on CoNLL-2014 (test) and F 0.5 of 72.4/73.6 on BEA-2019 (test).Its inference speed is up to 10 times as fast as a Transformer-based seq2seq GEC system.The code and trained models are publicly available 1 .