Papers with CoNLL-2014 test set
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data (N19-1)
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| Challenge: | Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. |
| Approach: | They propose a copy-augmented architecture for the Grammatical Error Correction task by copying unchanged words from the source sentence to the target sentence. |
| Outcome: | The proposed architecture outperforms all recently published state-of-the-art results by a large margin. |
An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction (D19-1)
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| Challenge: | incorporating pseudo data in the training of grammatical error correction models has been a key factor in improving performance of such models. |
| Approach: | They investigate the choice of how pseudo data should be generated or used in a grammatical error correction model and show that the results are state-of-the-art. |
| Outcome: | The proposed method achieves state-of-the-art on the CoNLL-2014 test set and the official test set of the BEA-2019 shared task without making any modifications to the model architecture. |
Frustratingly Easy System Combination for Grammatical Error Correction (2022.naacl-main)
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| Challenge: | Using a simple logistic regression algorithm, we combine GEC models for binary classification. |
| Approach: | They propose a logistic regression algorithm that can combine GEC models with binary classification. |
| Outcome: | The proposed method outperforms the state-of-the-art by 4.2 points on the CoNLL-2014 and 7.2 points on BEA-2019 test sets. |
Grammatical Error Correction: Are We There Yet? (2022.coling-1)
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| Challenge: | grammatical error correction (GEC) systems outperform humans on the CoNLL-2014 test set, but there are still classes of errors that they fail to correct. |
| Approach: | They found that state-of-the-art GEC systems outperform humans by a wide margin on the CoNLL-2014 test set . however, they found that there are still classes of errors that they fail to correct . |
| Outcome: | The F0.5 evaluation metric outperforms the CoNLL-2014 test set, but there are still classes of errors that they fail to correct. |
Neural Grammatical Error Correction with Finite State Transducers (N19-1)
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| Challenge: | Language model based GEC (LM-GEC) is a promising alternative to SMT and neural sequence-to-sequence models. |
| Approach: | They propose to use finite state transducers to improve LM-GEC by rescoring with neural language models. |
| Outcome: | The proposed model outperforms the best published results on the CoNLL-2014 test set and achieves far better relative improvements over the baselines. |
System Combination via Quality Estimation for Grammatical Error Correction (2023.emnlp-main)
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| Challenge: | Existing quality estimation models are not good enough to distinguish good corrections from bad ones, resulting in low F0.5 scores when used for system combination. |
| Approach: | They propose a new quality estimation model that gives a better estimate of the quality of a corrected sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art on the CoNLL-2014 and BEA-2019 test sets, and achieves the highest F0.5 scores published to date. |