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. |
Similar Papers
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. |
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. |
Data Weighted Training Strategies for Grammatical Error Correction (2020.tacl-1)
Copied to clipboard
| Challenge: | Recent advances in the task of Grammatical Error Correction (GEC) have been driven by addressing data sparsity, both through new methods for generating large and noisy pretraining data and through the publication of small and higher-quality finetuning data in the BEA-2019 shared task. |
| Approach: | They propose to incorporate delta-log-perplexity, a type of example scoring, into a training schedule for Grammatical Error Correction (GEC) they perform experiments that shed light on the function and applicability of delta- log-perplicity. |
| Outcome: | The proposed methods incorporate delta-log-perplexity, a type of example scoring, into a training schedule for the task. |
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches for grammatical error correction (GEC) rely on supervised learning with manually created datasets. |
| Approach: | They propose to denoise GEC datasets by leveraging prediction consistency of existing models. |
| Outcome: | The proposed method outperforms baseline methods on CoNLL-2014, JFLEG, and BEA-2019 benchmarks. |
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. |
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications (2021.emnlp-main)
Copied to clipboard
| Challenge: | Sentence-level Quality estimation (QE) is traditionally a regression task . but large multilingual contextualized language models are expensive and infeasible for real-world applications. |
| Approach: | They evaluate several model compression techniques for QE and find they are inefficient . they argue that a full model parameterization is required to achieve SoTA results . |
| Outcome: | The proposed models are poorly expressive in a regression task, the authors argue . they show that reframing QE as a classification problem and evaluating models would improve their performance in real-world applications. |
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. |
Are we Estimating or Guesstimating Translation Quality? (2020.acl-main)
Copied to clipboard
| Challenge: | A carefully engineered ensemble of pre-trained multilingual language models won the QE shared task at WMT19. |
| Approach: | They propose to use pre-trained multilingual language models to train quality estimation for machine translation. |
| Outcome: | A carefully engineered ensemble of pre-trained language models wins the QE shared task at WMT19. |
Revisiting Grammatical Error Correction Evaluation and Beyond (2022.emnlp-main)
Copied to clipboard
| Challenge: | Pretraining-based (PT) evaluation metrics are not effective for training grammatical error correction systems. |
| Approach: | They propose a pretraining-based GEC evaluation metric which only uses PT-based metrics to score the corrected parts of the system. |
| Outcome: | The proposed evaluation metric outperforms existing methods on a CoNLL14 evaluation task. |
Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and L1 (D19-55)
Copied to clipboard
| Challenge: | Grammar error correction systems have become ubiquitous in a variety of software applications, but little is known about how to efficiently personalize them to the user’s characteristics, such as proficiency level and first language. |
| Approach: | They propose to adapt a general purpose neural GEC system to the proficiency level and the first language of a writer, using only a few thousand annotated sentences. |
| Outcome: | The proposed system improves on adapting to proficiency level and first language . the results are the broadest of its kind, covering five proficiency levels and twelve different languages. |