JHU FLuency-Extended GUG corpus
JFLEG is for developing and evaluating grammatical error correction (GEC). Unlike other corpora, it represents a broad range of language proficiency levels and uses holistic fluency edits to not only correct grammatical errors but also make the original text more native sounding.
Source: JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
Variants: JFLEG, Restricted, Unrestricted, _Restricted_
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Grammatical Error Correction | VERNet | Neural Quality Estimation with Multiple … | 2021-05-10 |
| Grammatical Error Correction | Transformer + Pre-train with Pseudo Data + BERT | Encoder-Decoder Models Can Benefit from … | 2020-05-03 |
| Grammatical Error Correction | Copy-augmented Model (4 Ensemble +Denoising Autoencoder) | Improving Grammatical Error Correction via … | 2019-03-01 |
| Grammatical Error Correction | SMT + BiGRU | Near Human-Level Performance in Grammatical … | 2018-04-16 |
| Grammatical Error Correction | Transformer | Approaching Neural Grammatical Error Correction … | 2018-04-16 |
| Grammatical Error Correction | CNN Seq2Seq | A Multilayer Convolutional Encoder-Decoder Neural … | 2018-01-26 |
Recent papers with results on this dataset: