Papers with GEC system
A Crash Course in Automatic Grammatical Error Correction (2020.coling-tutorials)
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| Challenge: | Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text. |
| Approach: | tutorial aims to introduce participants to the field of Grammatical Error Correction . aim is to examine the development of neural-based GEC systems . |
| Outcome: | the tutorial aims to introduce participants to the current state of the art in the field of Grammatical Error Correction (GEC) |
Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation (N18-2)
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| Challenge: | Currently, most effective GEC systems are based on phrase-based statistical machine translation. |
| Approach: | They combine two of the most popular approaches to automated Grammatical Error Correction (GEC) they create a hybrid GEC system that preserves the accuracy of SMT output and generates more fluent sentences . |
| Outcome: | The proposed system achieves state-of-the-art on the CoNLL-2014 and JFLEG benchmarks. |
Are Pre-trained Language Models Useful for Model Ensemble in Chinese Grammatical Error Correction? (2023.acl-short)
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| Challenge: | Model ensemble has been in widespread use for Grammatical Error Correction (GEC), boosting model performance. |
| Approach: | They propose to use model ensembles computed by pre-trained language models to improve model performance. |
| Outcome: | The proposed ensembles do not improve but get worse after the PLM-based ensemble. |
Unsupervised Grammatical Error Correction Rivaling Supervised Methods (2023.emnlp-main)
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| Challenge: | Current state-of-the-art grammatical error correction systems rely on labeled data . current systems require manual correction and require a large quantity of labeles . |
| Approach: | They propose an unsupervised method to build a grammatical error correction system using a fixer and a critic. |
| Outcome: | The proposed system outperforms previous unsupervised systems on English and Chinese GEC. |
Neural Quality Estimation of Grammatical Error Correction (D18-1)
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| 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. |
Enhancing Grammatical Error Correction Systems with Explanations (2023.acl-long)
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| Challenge: | To help language learners better understand why the GEC system makes a correction, the causes of errors and the corresponding error types are two key factors. |
| Approach: | They propose to annotate large dataset with evidence words and grammatical error types to help language learners better understand corrections. |
| Outcome: | The proposed model can be validated by human evaluation and can be used to help second-language learners decide whether to accept a correction suggestion and understand the associated grammar rule. |
Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems (2021.emnlp-main)
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| 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. |
Improving Autoregressive Grammatical Error Correction with Non-autoregressive Models (2023.findings-acl)
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| Challenge: | Autoregressive models assign low probabilities to tokens that need corrections . grammatical error correction (GEC) is widely applied to natural language processing tasks . |
| Approach: | They propose to use a non-autoregressive model as an auxiliary model to train GEC models to correct grammatical errors in sentences. |
| Outcome: | The proposed method outperforms baselines on English and Chinese GEC tasks significantly. |
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. |
RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation (2023.emnlp-main)
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| Challenge: | Grammatical Error Correction (GEC) systems perform well in academic benchmarks, but in practical applications they may not correct errors when users perform irrelevant modifications. |
| Approach: | They propose a benchmark to evaluate the context robustness of Grammatical Error Correction systems. |
| Outcome: | The proposed method improves the accuracy of errors corrected by human annotations. |