Abstract
Both grammatical error correction and text style transfer can be viewed as monolingual sequence-to-sequence transformation tasks, but the scarcity of directly annotated data for either task makes them unfeasible for most languages. We present an approach that does both tasks within the same trained model, and only uses regular language parallel data, without requiring error-corrected or style-adapted texts. We apply our model to three languages and present a thorough evaluation on both tasks, showing that the model is reliable for a number of error types and style transfer aspects.
Abstract (translated)
语法纠错和文本样式转换都可以看作是单语序列到序列转换任务，但是由于两种任务都缺少直接注释的数据，因此大多数语言都无法实现。我们提出了一种方法，在同一个训练模型中执行两个任务，并且只使用常规语言并行数据，而不需要更正错误或调整样式的文本。我们将模型应用于三种语言，并对这两种任务进行了深入的评估，表明该模型在许多错误类型和样式转换方面都是可靠的。
URL
https://arxiv.org/abs/1903.11283
