Grammatical Error Correction: Machine Translation and Classifiers
Grammatical Error Correction: Machine Translation and Classifiers
复制标题
语法错误纠正：机器翻译和分类器
DOI：
10.18653/v1/p16-1208
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
D. Roth
中科院分区：
文献类型：
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作者：
Alla Rozovskaya;D. Roth
我们专注于两种领先的语法错误纠正方法-机器学习分类和机器翻译。基于这两个学习框架的比较研究，并通过对最先进的系统输出的错误分析，我们确定了这些方法中的每一种的关键优势和劣势，并证明了它们的互补性。特别是，机器翻译方法从并行数据中学习，而不需要进一步的语言输入，并且更擅长纠正复杂的错误。分类方法还具有其他理想的特性，例如能够轻松地概括训练中看到的内容，能够在没有人工注释的数据的情况下进行训练，以及能够灵活地调整知识源以适应各个错误类型。基于这种分析，我们开发了一种算法方法，结合了这两种方法的优势。我们提出了几个系统的基础上，在以前的工作中使用的资源，相对于以前的国家的最先进的改进超过20%（和7.4 F得分点）。
We focus on two leading state-of-the-art approaches to grammatical error correction – machine learning classification and machine translation. Based on the comparative study of the two learning frameworks and through error analysis of the output of the state-of-the-art systems, we identify key strengths and weaknesses of each of these approaches and demonstrate their complementarity. In particular, the machine translation method learns from parallel data without requiring further linguistic input and is better at correcting complex mistakes. The classification approach possesses other desirable characteristics, such as the ability to easily generalize beyond what was seen in training, the ability to train without human-annotated data, and the flexibility to adjust knowledge sources for individual error types. Based on this analysis, we develop an algorithmic approach that combines the strengths of both methods. We present several systems based on resources used in previous work with a relative improvement of over 20% (and 7.4 F score points) over the previous state-of-the-art.