Grammatical Error Correction
121 papers with code • 11 benchmarks • 15 datasets
Grammatical Error Correction (GEC) is the task of correcting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors.
GEC is typically formulated as a sentence correction task. A GEC system takes a potentially erroneous sentence as input and is expected to transform it to its corrected version. See the example given below:
| Input (Erroneous) | Output (Corrected) |
|---|---|
| She see Tom is catched by policeman in park at last night. | She saw Tom caught by a policeman in the park last night. |
Benchmarks
These leaderboards are used to track progress in Grammatical Error Correction
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Libraries
Use these libraries to find Grammatical Error Correction models and implementationsDatasets
CoNLL
JFLEG
CoNLL-2014 Shared Task: Grammatical Error Correction
WI-LOCNESS
GitHub Typo Corpus
Most implemented papers
Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality
The field of grammatical error correction (GEC) has grown substantially in recent years, with research directed at both evaluation metrics and improved system performance against those metrics.
There's No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction
We show that reference-less grammaticality metrics correlate very strongly with human judgments and are competitive with the leading reference-based evaluation metrics.
JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for developing and evaluating grammatical error correction (GEC).
Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated.
Systematically Adapting Machine Translation for Grammatical Error Correction
Our model rivals the current state of the art using a fraction of the training data.
Visual Text Correction
amirmazaheri1990/Visual-Text-Correction
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ECCV 2018
A semantic inconsistency between the sentence and the video or between the words of a sentence can result in an inaccurate description.
Reference-less Measure of Faithfulness for Grammatical Error Correction
We propose USim, a semantic measure for Grammatical Error Correction (GEC) that measures the semantic faithfulness of the output to the source, thereby complementing existing reference-less measures (RLMs) for measuring the output's grammaticality.
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.
Automatic Metric Validation for Grammatical Error Correction
Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between human and metric-induced rankings.
Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplification
The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality ({\it low coverage bias} or LCB).
