We improve automatic correction of grammatical, orthographic, and collocation errors in text using a multilayer convolutional encoder-decoder neural network. The network is initialized with embeddings that make use of character N-gram information to better suit this task. When evaluated on common benchmark test data sets (CoNLL-2014 and JFLEG), our model substantially outperforms all prior neural approaches on this task as well as strong statistical machine translation-based systems with neural and task-specific features trained on the same data. Our analysis shows the superiority of convolutional neural networks over recurrent neural networks such as long short-term memory (LSTM) networks in capturing the local context via attention, and thereby improving the coverage in correcting grammatical errors. By ensembling multiple models, and incorporating an N-gram language model and edit features via rescoring, our novel method becomes the first neural approach to outperform the current state-of-the-art statistical machine translation-based approach, both in terms of grammaticality and fluency.

本研究提出了一种基于多层卷积编码-解码神经网络的自动文本纠错方法，利用字符N-gram信息来初始化embeddings，通过attention机制捕捉局部文本语境并使用N-gram语言模型和编辑特征进行打分和融合，实现了比现有基于统计学习的方法更好的语法和流畅度的纠错效果。

一种多层卷积编码-解码神经网络用于语法错误纠正