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Korean Grammatical Error Correction Based on Transformer with Copying Mechanisms and Grammatical Noise Implantation Methods [PDF]
Sensors, 2021 Grammatical Error Correction (GEC) is the task of detecting and correcting various grammatical errors in texts. Many previous approaches to the GEC have used various mechanisms including rules, statistics, and their combinations.
Myunghoon Lee +3 more
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Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality [PDF]
Transactions of the Association for Computational Linguistics, 2021 The field of grammatical error correction (GEC) has grown substantially in recent years, with research directed at both evaluation metrics and improved system performance against those metrics. One unvisited assumption, however, is the reliance of GEC evaluation on error-coded corpora, which contain specific labeled corrections.
Keisuke Sakaguchi +3 more
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Evaluating LLMs' grammatical error correction performance in learner Chinese. [PDF]
PLoS ONELarge language models (LLMs) have recently exhibited significant capabilities in various English NLP tasks. However, their performance in Chinese grammatical error correction (CGEC) remains unexplored.
Sha Lin
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Tibyan corpus: balanced and comprehensive error coverage corpus using ChatGPT for Arabic grammatical error correction [PDF]
PeerJ Computer ScienceNatural language processing (NLP) augments text data to overcome sample size constraints. Scarce and low-quality data present particular challenges when learning from these domains.
Ahlam Alrehili, Areej Alhothali
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A method for English paragraph grammar correction based on differential fusion of syntactic features. [PDF]
PLoS ONEThe new progress of deep learning and natural language processing technology has strongly promoted the development of English grammar error correction. However, the existing methods mostly rely on large-scale corpus, and often ignore the fine syntactic ...
Weiling Liu +7 more
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Revisiting Meta-evaluation for Grammatical Error Correction
Transactions of the Association for Computational LinguisticsAbstract Metrics are the foundation for automatic evaluation in grammatical error correction (GEC), with their evaluation of the metrics (meta-evaluation) relying on their correlation with human judgments. However, conventional meta-evaluations in English GEC encounter several challenges, including biases caused by inconsistencies in ...
Masamune Kobayashi +2 more
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Adversarial Grammatical Error Correction [PDF]
Findings of the Association for Computational Linguistics: EMNLP 2020, 2020 Recent works in Grammatical Error Correction (GEC) have leveraged the progress in Neural Machine Translation (NMT), to learn rewrites from parallel corpora of grammatically incorrect and corrected sentences, achieving state-of-the-art results. At the same time, Generative Adversarial Networks (GANs) have been successful in generating realistic texts ...
Vipul Raheja, Dimitris Alikaniotis
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Chinese Grammatical Error Correction Based on Grammatical Knowledge Enhancement [PDF]
Jisuanji gongcheng, 2023 The aim of grammatical error correction is to judge whether natural language texts contain grammatical errors, to correct them. In recent years, with the rapid development of pre-trained language models, methods based on such models have been widely used
Qian DENG, Shu CHEN, Junmin YE
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Do Grammatical Error Correction Models Realize Grammatical Generalization? [PDF]
Journal of Natural Language Processing, 2021 There has been an increased interest in data generation approaches to grammatical error correction (GEC) using pseudo data. However, these approaches suffer from several issues that make them inconvenient for real-world deployment including a demand for large amounts of training data.
Mita, Masato, Yanaka, Hitomi
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GECToR – Grammatical Error Correction: Tag, Not Rewrite [PDF]
Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications, 2020 In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: first on errorful corpora, and second on a combination of errorful and error-free parallel corpora.
Omelianchuk, Kostiantyn +3 more
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