Grammatical error correction (GEC) suffers from a lack of sufficient parallel
data. Therefore, GEC studies have developed various methods to generate pseudo
data, which comprise pairs of grammatical and artificially produced
ungrammatical sentences. Currently, a mainstream approach to generate pseudo
data is back-translation (BT). Most previous GEC studies using BT have employed
the same architecture for both GEC and BT models. However, GEC models have
different correction tendencies depending on their architectures. Thus, in this
study, we compare the correction tendencies of the GEC models trained on pseudo
data generated by different BT models, namely, Transformer, CNN, and LSTM. The
results confirm that the correction tendencies for each error type are
different for every BT model. Additionally, we examine the correction
tendencies when using a combination of pseudo data generated by different BT
models. As a result, we find that the combination of different BT models
improves or interpolates the F_0.5 scores of each error type compared with that
of single BT models with different seeds.

本文主要探讨使用不同的 back-translation 模型为基础生成的 pseudo data 对语法纠错模型的影响和差异，实验结果表明：不同模型生成的数据对不同错误类型的纠错趋势影响不同；而使用不同模型的组合生成的数据能够提高或插值单一模型的 F0.5 得分。

使用后翻译模型进行语法错误纠正比较

Comparison of Grammatical Error Correction Using Back-Translation Models

Studies on grammatical error correction (GEC) have reported the effectiveness
of pretraining a Seq2Seq model with a large amount of pseudodata. However, this
approach requires time-consuming pretraining for GEC because of the size of the
pseudodata. In this study, we explore the utility of bidirectional and
auto-regressive transformers (BART) as a generic pretrained encoder-decoder
model for GEC. With the use of this generic pretrained model for GEC, the
time-consuming pretraining can be eliminated. We find that monolingual and
multilingual BART models achieve high performance in GEC, with one of the
results being comparable to the current strong results in English GEC. Our
implementations are publicly available at GitHub
(this https URL).

本研究探索了使用双向和自回归转换器 (BART) 作为通用预训练编码器 - 解码器模型的实用性，以解决语法错误纠正问题中需要长时间预训练的问题，并发现单语和多语言 BART 模型在语法错误纠正方面取得了很高的表现。