A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction

Code

• github.com/nusnlp/mlconvgec2018
  OfficialIn papernone★ 0
• github.com/seaweiqing/image2story
  tf★ 0
• github.com/seaweiqing/neuraltalk_plus_charcnn
  tf★ 0

Abstract

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.

Tasks

Benchmark Results

Reproductions