Grammatical error correction: A survey of the state of the art
Abstract Grammatical Error Correction (GEC) is the task of automatically detecting and
correcting errors in text. The task not only includes the correction of grammatical errors, such …
correcting errors in text. The task not only includes the correction of grammatical errors, such …
Neural machine translation: A review
F Stahlberg - Journal of Artificial Intelligence Research, 2020 - jair.org
The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …
natural language into another, has experienced a major paradigm shift in recent years …
Token-level self-evolution training for sequence-to-sequence learning
Adaptive training approaches, widely used in sequence-to-sequence models, commonly
reweigh the losses of different target tokens based on priors, eg word frequency. However …
reweigh the losses of different target tokens based on priors, eg word frequency. However …
A multilayer convolutional encoder-decoder neural network for grammatical error correction
We improve automatic correction of grammatical, orthographic, and collocation errors in text
using a multilayer convolutional encoder-decoder neural network. The network is initialized …
using a multilayer convolutional encoder-decoder neural network. The network is initialized …
A comprehensive survey of grammatical error correction
Grammatical error correction (GEC) is an important application aspect of natural language
processing techniques, and GEC system is a kind of very important intelligent system that …
processing techniques, and GEC system is a kind of very important intelligent system that …
TemplateGEC: Improving grammatical error correction with detection template
Grammatical error correction (GEC) can be divided into sequence-to-edit (Seq2Edit) and
sequence-to-sequence (Seq2Seq) frameworks, both of which have their pros and cons. To …
sequence-to-sequence (Seq2Seq) frameworks, both of which have their pros and cons. To …
Fluency boost learning and inference for neural grammatical error correction
Most of the neural sequence-to-sequence (seq2seq) models for grammatical error correction
(GEC) have two limitations:(1) a seq2seq model may not be well generalized with only …
(GEC) have two limitations:(1) a seq2seq model may not be well generalized with only …