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 …
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 …
The BEA-2019 shared task on grammatical error correction
C Bryant, M Felice, ØE Andersen… - Proceedings of the …, 2019 - aclanthology.org
This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC).
As with the CoNLL-2014 shared task, participants are required to correct all types of errors in …
As with the CoNLL-2014 shared task, participants are required to correct all types of errors in …
Neural grammatical error correction systems with unsupervised pre-training on synthetic data
Considerable effort has been made to address the data sparsity problem in neural
grammatical error correction. In this work, we propose a simple and surprisingly effective …
grammatical error correction. In this work, we propose a simple and surprisingly effective …
Approaching neural grammatical error correction as a low-resource machine translation task
Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-
art results compared to phrase-based statistical machine translation (SMT) baselines. We …
art results compared to phrase-based statistical machine translation (SMT) baselines. We …
Seq2Edits: Sequence transduction using span-level edit operations
F Stahlberg, S Kumar - arxiv preprint arxiv:2009.11136, 2020 - arxiv.org
We propose Seq2Edits, an open-vocabulary approach to sequence editing for natural
language processing (NLP) tasks with a high degree of overlap between input and output …
language processing (NLP) tasks with a high degree of overlap between input and output …
Neural language correction with character-based attention
Natural language correction has the potential to help language learners improve their
writing skills. While approaches with separate classifiers for different error types have high …
writing skills. While approaches with separate classifiers for different error types have high …
Near human-level performance in grammatical error correction with hybrid machine translation
We combine two of the most popular approaches to automated Grammatical Error
Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC based on …
Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC based on …
Phrase-based machine translation is state-of-the-art for automatic grammatical error correction
In this work, we study parameter tuning towards the M^ 2 metric, the standard metric for
automatic grammar error correction (GEC) tasks. After implementing M^ 2 as a scorer in the …
automatic grammar error correction (GEC) tasks. After implementing M^ 2 as a scorer in the …
Data weighted training strategies for grammatical error correction
Recent progress in the task of Grammatical Error Correction (GEC) has been driven by
addressing data sparsity, both through new methods for generating large and noisy …
addressing data sparsity, both through new methods for generating large and noisy …