System Combination via Quality Estimation for Grammatical Error Correction (2023.emnlp-main)
Copied to clipboard
| 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. |
Similar Papers
Frustratingly Easy System Combination for Grammatical Error Correction (2022.naacl-main)
Copied to clipboard
| 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. |
Neural Quality Estimation of Grammatical Error Correction (D18-1)
Copied to clipboard
| Challenge: | Grammatical error correction systems are expected to correct most learners’ writing errors, but in practice they often produce spurious corrections and fail to correct many errors, thereby misleading learners. |
| Approach: | They propose to use supervised learning to estimate the quality of GEC output sentences to help instructors decide whether to correct the errors or ignore them altogether. |
| Outcome: | The proposed model improves on a feature-based baseline and shows that the state-of-the-art system can be improved when quality scores are used as features for re-ranking the N-best candidates. |
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing GEC models produce spurious corrections or fail to detect lots of errors. |
| Approach: | They propose a neural network for GEC quality estimation with multiple hypotheses . VERNet establishes interactions among hypothese based on reasoning graph . |
| Outcome: | The proposed model achieves state-of-the-art grammatical error detection performance and best quality estimation results on four GEC datasets. |
How Good (really) are Grammatical Error Correction Systems? (2021.eacl-main)
Copied to clipboard
| Challenge: | Standard evaluations of Grammatical Error Correction systems use a fixed reference text generated relative to the original text. |
| Approach: | They propose to use a gold reference text to evaluate Grammatical Error Correction systems that is generated relative to the original text and is independent of the system output. |
| Outcome: | The proposed evaluations show that the system performs 20-40 points better than standard evaluations. |
ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction (2022.lrec-1)
Copied to clipboard
| Challenge: | Prior work has shown that QE models of grammatical error correction are biased toward data by learners with relatively high proficiency levels. |
| Approach: | They investigated whether learners' proficiency affects supervised quality estimation models of grammatical error correction (GEC) . they created a QE dataset that includes multiple proficiency levels and explored the necessity of performing proficiency-wise evaluation for QE of GEC. |
| Outcome: | The proposed model is based on multiple proficiency levels and can be performed in real-world scenarios. |
Is this the end of the gold standard? A straightforward reference-less grammatical error correction metric (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing evaluations of grammatical error correction systems use reference-based metrics, but they are limited because of multiple correct outputs. |
| Approach: | They propose a system that uses commonly available tools to evaluate grammatical error correction (GEC) systems. |
| Outcome: | The proposed system solves the issues related to the use of a reference and does not need another annotated dataset for fine-tuning. |
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction (2022.lrec-1)
Copied to clipboard
Daisuke Suzuki, Yujin Takahashi, Ikumi Yamashita, Taichi Aida, Tosho Hirasawa, Michitaka Nakatsuji, Masato Mita, Mamoru Komachi
| Challenge: | Existing studies on automatic evaluation of grammatical error correction (GEC) have shown that quality estimation models built from manual evaluation can achieve high performance in automatic evaluation in English. |
| Approach: | They used a dataset with manual evaluation to build an automatic evaluation model for Japanese GEC. |
| Outcome: | The proposed model is based on a Japanese dataset with manual evaluation and meta-evaluation. |
Improving Grammatical Error Correction with Machine Translation Pairs (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to generate error-corrected sentence pairs for improving grammatical error correction are not available. |
| Approach: | They propose a method to generate error-corrected sentence pairs for improving grammatical error correction based on machine translation models of different qualities . |
| Outcome: | The proposed method can generate multiple error-corrected sentence pairs from Chinese to English text. |
Grammatical Error Correction: Are We There Yet? (2022.coling-1)
Copied to clipboard
| 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. |
Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems (2021.emnlp-main)
Copied to clipboard
| Challenge: | a multi-class grammatical error detection system can be used to improve grammamatical errors correction (GEC) for English. |
| Approach: | They develop a multi-class grammatical error detection system based on pre-trained ELECTRA and extend it to multi-Class detection using different error type tagsets. |
| Outcome: | The proposed system outperforms previous systems on the BEA-test benchmark. |