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
Sequence Classification with Human Attention
coastalcph/Sequence_classification_with_human_attention
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CONLL 2018
Learning attention functions requires large volumes of data, but many NLP tasks simulate human behavior, and in this paper, we show that human attention really does provide a good inductive bias on many attention functions in NLP.
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection
skasewa/wronging
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EMNLP 2018
Grammatical error correction, like other machine learning tasks, greatly benefits from large quantities of high quality training data, which is typically expensive to produce.
Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings
In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns.
Semi-supervised Multitask Learning for Sequence Labeling
marekrei/sequence-labeler
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ACL 2017
We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset.
