标题： Transformer语言模型在语法错误更正中的非合理有效性 显示英文标题

标题： The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction

摘要： 最近在语法错误更正（GEC）方面的工作强调了语言建模的重要性，因为通过比较提议的修改的概率，显然可以实现良好的性能。 与此同时，语言建模的进步已经能够生成几乎无法与人类生成的文本区分开的语言输出。 在本文中，我们通过探索更复杂的语言模型在GEC中的潜力，并提供了一些关于它们优缺点的关键见解。 我们表明，与其它自然语言处理任务中的最新结果一致，Transformer架构表现出稳定高性能，并为未来的机器学习模型提供了有竞争力的基线。 显示英文摘要

摘要： Recent work on Grammatical Error Correction (GEC) has highlighted the importance of language modeling in that it is certainly possible to achieve good performance by comparing the probabilities of the proposed edits. At the same time, advancements in language modeling have managed to generate linguistic output, which is almost indistinguishable from that of human-generated text. In this paper, we up the ante by exploring the potential of more sophisticated language models in GEC and offer some key insights on their strengths and weaknesses. We show that, in line with recent results in other NLP tasks, Transformer architectures achieve consistently high performance and provide a competitive baseline for future machine learning models.