论文标题
个性化语法错误校正：适应能力水平和L1
Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and L1
论文作者
论文摘要
语法误差校正（GEC）系统在各种软件应用程序中已变得无处不在，并已开始对某些数据集进行人级的性能。但是，如何有效地将这些系统个性化为用户的特征，例如其熟练程度和第一语言或文本的新兴域，知之甚少。我们提出了将通用神经GEC系统调整为熟练程度和作者的第一语言的第一个结果，仅使用几千个注释的句子。我们的研究是同类研究中最广泛的研究，涵盖了五个能力水平和十二种不同的语言，并比较了三种不同的适应情景：仅适应熟练程度，仅适用于第一语言，或同时对两种方面。我们表明，针对两种情况的裁缝都相对于强大的基线实现了最大的性能提高（3.6 F0.5）。
Grammar error correction (GEC) systems have become ubiquitous in a variety of software applications, and have started to approach human-level performance for some datasets. However, very little is known about how to efficiently personalize these systems to the user's characteristics, such as their proficiency level and first language, or to emerging domains of text. We present the first results on adapting a general-purpose neural GEC system to both the proficiency level and the first language of a writer, using only a few thousand annotated sentences. Our study is the broadest of its kind, covering five proficiency levels and twelve different languages, and comparing three different adaptation scenarios: adapting to the proficiency level only, to the first language only, or to both aspects simultaneously. We show that tailoring to both scenarios achieves the largest performance improvement (3.6 F0.5) relative to a strong baseline.
