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natural-language-processing-11

Grammatical Error Detection

3260 papers • 126 benchmarks • 313 datasets

Grammatical Error Detection (GED) is the task of detecting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors. Grammatical error detection (GED) is one of the key component in grammatical error correction (GEC) community.

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Benchmarks

These leaderboards are used to track progress in grammatical-error-detection-11

Trend

Dataset

Best Model

Actions

FCE

FCE

CoNLL-2014 A1

CoNLL-2014 A1

CoNLL-2014 A2

CoNLL-2014 A2

Libraries

i

Use these libraries to find grammatical-error-detection-11 models and implementations

Datasets

CoNLL

FCE

JFLEG

FCGEC

Subtasks

No subtasks available.

Most implemented papers

Semi-supervised Multitask Learning for Sequence Labeling

Marek Rei•Sun Apr 23 2017

A sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset, which incentivises the system to learn general-purpose patterns of semantic and syntactic composition, useful for improving accuracy on different sequence labeling tasks.

251

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

JFLEG

JFLEG

0

Jointly Learning to Label Sentences and Tokens

Anders Søgaard, Marek Rei•Tue Nov 13 2018

This paper investigates how objectives at different granularities can be used to learn better language representations and proposes an architecture for jointly learning to label sentences and tokens and achieves substantial improvements for both sentence classification and sequence labeling.

40 0

FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction

Jian Wu, Lvxiaowei Xu, Jiawei Peng, Jiayu Fu, Ming Cai•Fri Oct 21 2022

Grammatical Error Correction (GEC) has been broadly applied in automatic correction and proofreading system recently. However, it is still immature in Chinese GEC due to limited high-quality data from native speakers in terms of category and scale. In this paper, we present FCGEC, a fine-grained corpus to detect, identify and correct the grammatical errors. FCGEC is a human-annotated corpus with multiple references, consisting of 41,340 sentences collected mainly from multi-choice questions in public school Chinese examinations. Furthermore, we propose a Switch-Tagger-Generator (STG) baseline model to correct the grammatical errors in low-resource settings. Compared to other GEC benchmark models, experimental results illustrate that STG outperforms them on our FCGEC. However, there exists a significant gap between benchmark models and humans that encourages future models to bridge it.

21 0

Bangla Grammatical Error Detection Using T5 Transformer Model

H. S. Shahgir, Khondker Salman Sayeed•Sat Mar 18 2023

The T5 model was primarily designed for translation and is not specifically designed for this task, so extensive post-processing was necessary to adapt it to the task of error detection, and it can achieve low Levenshtein Distance.

5 0

Towards a standard evaluation method for grammatical error detection and correction

Ted Briscoe, Mariano Felice•Wed Dec 31 2014

A novel evaluation method for grammatical error correction that addresses problems with previous approaches and scores systems in terms of improvement on the original text by evaluating corrections at the token level using a globally optimal alignment between the source, a system hypothesis, and a reference.

69 0

Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings

Mamoru Komachi, Masahiro Kaneko, Yuya Sakaizawa•Tue Oct 31 2017

A bidirectional long-short term memory model initialized by word embeddings achieved the state-of-the-art accuracy by a large margin in an English grammatical error detection task on the First Certificate in English dataset.

29 0

Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection

Pontus Stenetorp, Sebastian Riedel, Sudhanshu Kasewa•Tue Sep 25 2018

This work investigates cheaply constructing synthetic samples, given a small corpus of human-annotated data, using an off-the-rack attentive sequence-to-sequence model and a straight-forward post-processing procedure, and yields error-filled artificial data that helps a vanilla bi-directional LSTM to outperform the previous state of the art at grammatical error detection.

60 0

Sequence Classification with Human Attention

Anders Søgaard, Joachim Bingel, Marek Rei, Nora Hollenstein, Maria Barrett•Sun Sep 30 2018

Estimated human attention derived from eye-tracking corpora is used to regularize attention functions in recurrent neural networks and shows substantial improvements across a range of tasks, including sentiment analysis, grammatical error detection, and detection of abusive language.

100 0

Context is Key: Grammatical Error Detection with Contextual Word Representations

H. Yannakoudakis, Marek Rei, Samuel J Bell•Fri May 31 2019

A systematic comparison of ELMo, BERT and Flair embeddings on a range of public GED datasets are performed, and an approach to effectively integrate such representations in current methods is proposed, achieving a new state of the art on GED.

46 0

Adding a benchmark result helps the community track progress.

Grammatical Error Detection | State-of-the-Art