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.

本文介绍了一种使用 Transformer 编码器的简单高效的 GEC 序列标记工具，该系统采用合成数据进行预训练，并在连续两个阶段上进行微调，第一阶段在有错误的语料库上，第二阶段在有错误和无错误的平行语料库上。使用自定义的 token-level 变换将输入 token 映射到目标纠正 。我们的最佳单模型 / 集合 GEC 标记器在 CoNLL-2014（测试）上的 $F_{0.5}$ 为 65.3 / 66.5，在 BEA-2019（测试）上为 $F_{0.5}$ 的 72.4 / 73.6。其推理速度比基于 Transformer 的 seq2seq GEC 系统快 10 倍。代码和训练模型公开可用。