论文标题
使用验证的编码器模型进行语法误差校正的更强基准
Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model
论文作者
论文摘要
关于语法误差校正（GEC）的研究报告了用大量假dat缩预处理SEQ2SEQ模型的有效性。但是，由于伪死的大小，这种方法需要对GEC进行耗时的预处理。在这项研究中，我们探讨了双向和自动回归变压器（BART）作为GEC的通用编码器模型的实用性。通过使用此通用预审预周化的GEC模型，可以消除耗时的预训练。我们发现单语和多语言巴特模型在GEC中实现了高性能，其中一个结果与当前的英语GEC相当。我们的实现可在GitHub（https://github.com/katsumata420/generic-pretraining-gec）上公开获得。
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 (https://github.com/Katsumata420/generic-pretrained-GEC).
