Geodesy

Context (archaeology)

Benchmark (surveying)

Linguistics

Philosophy

Machine translation

Biology

Grammaticality

Operating system

Economics

Grammar

Computer science

Language model

Paleontology

Natural language processing

Task (project management)

Management

Geography

Artificial intelligence

Artificial neural network

Convolutional neural network

Recurrent neural network

Machine learning

Encoder

Speech recognition

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.

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