Grammatical Error Detection
17 papers with code • 4 benchmarks • 4 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.
Benchmarks
These leaderboards are used to track progress in Grammatical Error Detection
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CoNLL
JFLEG
FCGEC
Latest papers
GECTurk: Grammatical Error Correction and Detection Dataset for Turkish
GGLAB-KU/gecturk
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To encourage further research on Turkish GEC, we release our datasets, baseline models, and the synthetic data generation pipeline at https://github. com/GGLAB-KU/gecturk.
Evaluation of really good grammatical error correction
We find that GPT-3 in a few-shot setting by far outperforms previous grammatical error correction systems for Swedish, a language comprising only 0. 11% of its training data.
Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation
We also define the task of multi-class Arabic grammatical error detection (GED) and present the first results on multi-class Arabic GED.
Bangla Grammatical Error Detection Using T5 Transformer Model
ramisa2108/bangla-complex-named-entity-recognition-challenge
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This paper presents a method for detecting grammatical errors in Bangla using a Text-to-Text Transfer Transformer (T5) Language Model, using the small variant of BanglaT5, fine-tuned on a corpus of 9385 sentences where errors were bracketed by the dedicated demarcation symbol.
Probing for targeted syntactic knowledge through grammatical error detection
chrisdavis90/ged-syntax-probing
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Targeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information.
FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction
xlxwalex/FCGEC
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Grammatical Error Correction (GEC) has been broadly applied in automatic correction and proofreading system recently.
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills.
Context is Key: Grammatical Error Detection with Contextual Word Representations
samueljamesbell/sequence-labeler
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WS 2019
Grammatical error detection (GED) in non-native writing requires systems to identify a wide range of errors in text written by language learners.
Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional Decomposition
From this sequence-labeling layer we derive dense representations of the input that can then be matched to instances from training, or a support set with known labels.
Jointly Learning to Label Sentences and Tokens
Learning to construct text representations in end-to-end systems can be difficult, as natural languages are highly compositional and task-specific annotated datasets are often limited in size.
