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Journal articles on the topic 'Grammatical error correction(GEC)'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Lee, Myunghoon, Hyeonho Shin, Dabin Lee, and Sung-Pil Choi. "Korean Grammatical Error Correction Based on Transformer with Copying Mechanisms and Grammatical Noise Implantation Methods." Sensors 21, no. 8 (2021): 2658. http://dx.doi.org/10.3390/s21082658.

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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. Recently, the performance of the GEC in English has been drastically enhanced due to the vigorous applications of deep neural networks and pretrained language models. Following the promising results of the English GEC tasks, we apply the Transformer with Copying Mechanism into the Korean GEC task by introducing novel and effective noising methods for constructing Kore

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2

Wang, Yu, Yuelin Wang, Kai Dang, Jie Liu, and Zhuo Liu. "A Comprehensive Survey of Grammatical Error Correction." ACM Transactions on Intelligent Systems and Technology 12, no. 5 (2021): 1–51. http://dx.doi.org/10.1145/3474840.

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Grammatical error correction (GEC) is an important application aspect of natural language processing techniques, and GEC system is a kind of very important intelligent system that has long been explored both in academic and industrial communities. The past decade has witnessed significant progress achieved in GEC for the sake of increasing popularity of machine learning and deep learning. However, there is not a survey that untangles the large amount of research works and progress in this field. We present the first survey in GEC for a comprehensive retrospective of the literature in this area

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Kobayashi, Masamune, Masato Mita, and Mamoru Komachi. "Revisiting Meta-evaluation for Grammatical Error Correction." Transactions of the Association for Computational Linguistics 12 (2024): 837–55. http://dx.doi.org/10.1162/tacl_a_00676.

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Abstract 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 evaluation granularity and an outdated setup using classical systems. These problems can lead to misinterpretation of metrics and potentially hinder the applicability of GEC techniques. To address these issues, this paper proposes SEEDA, a new dataset for GEC meta

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Zhao, Zewei, and Houfeng Wang. "MaskGEC: Improving Neural Grammatical Error Correction via Dynamic Masking." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1226–33. http://dx.doi.org/10.1609/aaai.v34i01.5476.

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Grammatical error correction (GEC) is a promising natural language processing (NLP) application, whose goal is to change the sentences with grammatical errors into the correct ones. Neural machine translation (NMT) approaches have been widely applied to this translation-like task. However, such methods need a fairly large parallel corpus of error-annotated sentence pairs, which is not easy to get especially in the field of Chinese grammatical error correction. In this paper, we propose a simple yet effective method to improve the NMT-based GEC models by dynamic masking. By adding random masks

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Wang, Hongfei, Zhousi Chen, Zizheng Zhang, et al. "Revisiting the Evaluation for Chinese Grammatical Error Correction." Journal of Advanced Computational Intelligence and Intelligent Informatics 28, no. 6 (2024): 1380–90. http://dx.doi.org/10.20965/jaciii.2024.p1380.

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English grammar error correction (GEC) has been a popular topic over the past decade. The appropriateness of automatic evaluations, e.g., the combination of metrics and reference types, has been thoroughly studied for English GEC. Yet, such systematic investigations on the Chinese GEC are still insufficient. Specifically, we noticed that two representative Chinese GEC evaluation datasets, namely YACLC and MuCGEC, adopt fluency edits-based references with the automatic evaluation metric, which was designed for minimal edits-based references and differs from the convention of English GEC. Howeve

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Park, Chanjun, Seonmin Koo, Gyeongmin Kim, and Heuiseok Lim. "Towards Harnessing the Most of ChatGPT for Korean Grammatical Error Correction." Applied Sciences 14, no. 8 (2024): 3195. http://dx.doi.org/10.3390/app14083195.

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In this study, we conduct a pioneering and comprehensive examination of ChatGPT’s (GPT-3.5 Turbo) capabilities within the realm of Korean Grammatical Error Correction (K-GEC). Given the Korean language’s agglutinative nature and its rich linguistic intricacies, the task of accurately correcting errors while preserving Korean-specific sentiments is notably challenging. Utilizing a systematic categorization of Korean grammatical errors, we delve into a meticulous, case-specific analysis to identify the strengths and limitations of a ChatGPT-based correction system. We also critically assess infl

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Starchenko, Vladimir, Darya Kharlamova, Elizaveta Klykova, et al. "Fighting Evaluation Inflation: Concentrated Datasets for Grammatical Error Correction Task." Journal of Language and Education 10, no. 4 (2024): 112–29. https://doi.org/10.17323/jle.2024.22272.

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Background: Grammatical error correction (GEC) systems have greatly developed over the recent decade. According to common metrics, they often reach the level of or surpass human experts. Nevertheless, they perform poorly on several kinds of errors that are effortlessly corrected by humans. Thus, reaching the resolution limit, evaluation algorithms and datasets do not allow for further enhancement of GEC systems. Purpose: To solve the problem of the resolution limit in GEC. The suggested approach is to use for evaluation concentrated datasets with a higher density of errors that are difficult f

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Sakaguchi, Keisuke, Courtney Napoles, Matt Post, and Joel Tetreault. "Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality." Transactions of the Association for Computational Linguistics 4 (December 2016): 169–82. http://dx.doi.org/10.1162/tacl_a_00091.

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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. We examine current practices and show that GEC’s reliance on such corpora unnaturally constrains annotation and automatic evaluation, resulting in (a) sentences that do not sound acceptable to native speakers and (b) system rankings that do not correlate with human jud

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9

Musyafa, Ahmad, Ying Gao, Aiman Solyman, Siraj Khan, Wentian Cai, and Muhammad Faizan Khan. "Dynamic decoding and dual synthetic data for automatic correction of grammar in low-resource scenario." PeerJ Computer Science 10 (July 5, 2024): e2122. http://dx.doi.org/10.7717/peerj-cs.2122.

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Grammar error correction systems are pivotal in the field of natural language processing (NLP), with a primary focus on identifying and correcting the grammatical integrity of written text. This is crucial for both language learning and formal communication. Recently, neural machine translation (NMT) has emerged as a promising approach in high demand. However, this approach faces significant challenges, particularly the scarcity of training data and the complexity of grammar error correction (GEC), especially for low-resource languages such as Indonesian. To address these challenges, we propos

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Kong, Zixiao, Xianquan Wang, Shuanghong Shen, Keyu Zhu, Huibo Xu, and Yu Su. "ScholarGEC: Enhancing Controllability of Large Language Model for Chinese Academic Grammatical Error Correction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24339–47. https://doi.org/10.1609/aaai.v39i23.34611.

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Large language models (LLMs) have demonstrated exceptional error detection capabilities and can correct sentences with high fluency in grammatical error correction (GEC) tasks. However, when correcting Chinese academic papers, LLMs face significant challenges of over-correction. To delve deeper into this issue, we explore the underlying reasons. On one hand, each discipline has its unique vocabulary and expressions, and LLMs have insufficient and incomplete understanding of domain-specific sentences. On the other hand, the controllability of generative LLMs in GEC tasks is inherently poor, and

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Lichtarge, Jared, Chris Alberti, and Shankar Kumar. "Data Weighted Training Strategies for Grammatical Error Correction." Transactions of the Association for Computational Linguistics 8 (October 2020): 634–46. http://dx.doi.org/10.1162/tacl_a_00336.

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Recent progress in the task of Grammatical Error Correction (GEC) has 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. Building upon recent work in Neural Machine Translation (NMT), we make use of both kinds of data by deriving example-level scores on our large pretraining data based on a smaller, higher-quality dataset. In this work, we perform an empirical study to discover how to best incorporate delta-log-perplexity, a type of

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Lin, Nankai, Boyu Chen, Xiaotian Lin, Kanoksak Wattanachote, and Shengyi Jiang. "A Framework for Indonesian Grammar Error Correction." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (2021): 1–12. http://dx.doi.org/10.1145/3440993.

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Grammatical Error Correction (GEC) is a challenge in Natural Language Processing research. Although many researchers have been focusing on GEC in universal languages such as English or Chinese, few studies focus on Indonesian, which is a low-resource language. In this article, we proposed a GEC framework that has the potential to be a baseline method for Indonesian GEC tasks. This framework treats GEC as a multi-classification task. It integrates different language embedding models and deep learning models to correct 10 types of Part of Speech (POS) error in Indonesian text. In addition, we co

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Musyafa, Ahmad, Ying Gao, Aiman Solyman, Chaojie Wu, and Siraj Khan. "Automatic Correction of Indonesian Grammatical Errors Based on Transformer." Applied Sciences 12, no. 20 (2022): 10380. http://dx.doi.org/10.3390/app122010380.

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Grammatical error correction (GEC) is one of the major tasks in natural language processing (NLP) which has recently attracted great attention from researchers. The performance of universal languages such as English and Chinese in the GEC system has improved significantly. This could be attributed to the large number of powerful applications supported by neural network models and pretrained language models. Referring to the satisfactory results of the universal language in the GEC task and the lack of research on the GEC task for low-resource languages, especially Indonesian, this paper propos

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Náplava, Jakub, Milan Straka, Jana Straková, and Alexandr Rosen. "Czech Grammar Error Correction with a Large and Diverse Corpus." Transactions of the Association for Computational Linguistics 10 (2022): 452–67. http://dx.doi.org/10.1162/tacl_a_00470.

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Abstract We introduce a large and diverse Czech corpus annotated for grammatical error correction (GEC) with the aim to contribute to the still scarce data resources in this domain for languages other than English. The Grammar Error Correction Corpus for Czech (GECCC) offers a variety of four domains, covering error distributions ranging from high error density essays written by non-native speakers, to website texts, where errors are expected to be much less common. We compare several Czech GEC systems, including several Transformer-based ones, setting a strong baseline to future research. Fin

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Golizadeh, Nassibeh, Mahdi Golizadeh, and Mohamad Forouzanfar. "Adversarial Grammatical Error Generation: Application to Persian Language." International Journal on Natural Language Computing 11, no. 4 (2022): 19–28. http://dx.doi.org/10.5121/ijnlc.2022.11402.

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Grammatical error correction (GEC) greatly benefits from large quantities of high-quality training data. However, the preparation of a large amount of labelled training data is time-consuming and prone to human errors. These issues have become major obstacles in training GEC systems. Recently, the performance of English GEC systems has drastically been enhanced by the application of deep neural networks that generate a large amount of synthetic data from limited samples. While GEC has extensively been studied in languages such as English and Chinese, no attempts have been made to generate synt

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Nassibeh, Golizadeh, Golizadeh Mahdi, and Forouzanfar Mohamad. "ADVERSARIAL GRAMMATICAL ERROR GENERATION: APPLICATION TO PERSIAN LANGUAGE." International Journal on Natural Language Computing (IJNLC) 11, no. 4 (2022): 10. https://doi.org/10.5281/zenodo.7085244.

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Grammatical error correction (GEC) greatly benefits from large quantities of high-quality training data. However, the preparation of a large amount of labelled training data is time-consuming and prone to human errors. These issues have become major obstacles in training GEC systems. Recently, the performance of English GEC systems has drastically been enhanced by the application of deep neural networks that generate a large amount of synthetic data from limited samples. While GEC has extensively been studied in languages such as English and Chinese, no attempts have been made to generate synt

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17

Xie, Jinxiang, Yilin Li, Xunjian Yin, and Xiaojun Wan. "DSGram: Dynamic Weighting Sub-Metrics for Grammatical Error Correction in the Era of Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25561–69. https://doi.org/10.1609/aaai.v39i24.34746.

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Evaluating the performance of Grammatical Error Correction (GEC) models has become increasingly challenging, as large language model (LLM)-based GEC systems often produce corrections that diverge from provided gold references. This discrepancy undermines the reliability of traditional reference-based evaluation metrics. In this study, we propose a novel evaluation framework for GEC models, DSGram, integrating Semantic Coherence, Edit Level, and Fluency, and utilizing a dynamic weighting mechanism. Our framework employs the Analytic Hierarchy Process (AHP) in conjunction with large language mod

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18

Ailani, Sagar, Ashwini Dalvi, and Irfan Siddavatam. "Grammatical Error Correction (GEC): Research Approaches till now." International Journal of Computer Applications 178, no. 40 (2019): 1–3. http://dx.doi.org/10.5120/ijca2019919275.

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19

Li, Yiyuan, Antonios Anastasopoulos, and Alan W. Black. "Towards Minimal Supervision BERT-Based Grammar Error Correction (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13859–60. http://dx.doi.org/10.1609/aaai.v34i10.7202.

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Current grammatical error correction (GEC) models typically consider the task as sequence generation, which requires large amounts of annotated data and limit the applications in data-limited settings. We try to incorporate contextual information from pre-trained language model to leverage annotation and benefit multilingual scenarios. Results show strong potential of Bidirectional Encoder Representations from Transformers (BERT) in grammatical error correction task.

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Ye, Jingheng, Shang Qin, Yinghui Li, et al. "EXCGEC: A Benchmark for Edit-Wise Explainable Chinese Grammatical Error Correction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25678–86. https://doi.org/10.1609/aaai.v39i24.34759.

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Existing studies explore the explainability of Grammatical Error Correction (GEC) in a limited scenario, where they ignore the interaction between corrections and explanations and have not established a corresponding comprehensive benchmark. To bridge the gap, this paper first introduces the task of EXplainable GEC (EXGEC), which focuses on the integral role of correction and explanation tasks. To facilitate the task, we propose EXCGEC, a tailored benchmark for Chinese EXGEC consisting of 8,216 explanation-augmented samples featuring the design of hybrid edit-wise explanations. We then benchma

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Li, Jiquan, Junliang Guo, Yongxin Zhu, et al. "Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 10974–82. http://dx.doi.org/10.1609/aaai.v36i10.21345.

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The task of Grammatical Error Correction (GEC) has received remarkable attention with wide applications in Natural Language Processing (NLP) in recent years. While one of the key principles of GEC is to keep the correct parts unchanged and avoid over-correction, previous sequence-to-sequence (seq2seq) models generate results from scratch, which are not guaranteed to follow the original sentence structure and may suffer from the over-correction problem. In the meantime, the recently proposed sequence tagging models can overcome the over-correction problem by only generating edit operations, but

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Napoles, Courtney, Maria Nădejde, and Joel Tetreault. "Enabling Robust Grammatical Error Correction in New Domains: Data Sets, Metrics, and Analyses." Transactions of the Association for Computational Linguistics 7 (November 2019): 551–66. http://dx.doi.org/10.1162/tacl_a_00282.

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Until now, grammatical error correction (GEC) has been primarily evaluated on text written by non-native English speakers, with a focus on student essays. This paper enables GEC development on text written by native speakers by providing a new data set and metric. We present a multiple-reference test corpus for GEC that includes 4,000 sentences in two new domains ( formal and informal writing by native English speakers) and 2,000 sentences from a diverse set of non-native student writing. We also collect human judgments of several GEC systems on this new test set and perform a meta-evaluation,

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Kuang, Hailan, Kewen Wu, Xiaolin Ma, and Xinhua Liu. "A Chinese Grammatical Error Correction Method Based on Iterative Training and Sequence Tagging." Applied Sciences 12, no. 9 (2022): 4364. http://dx.doi.org/10.3390/app12094364.

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Chinese grammatical error correction (GEC) is under continuous development and improvement, and this is a challenging task in the field of natural language processing due to the high complexity and flexibility of Chinese grammar. Nowadays, the iterative sequence tagging approach is widely applied to Chinese GEC tasks because it has a faster inference speed than sequence generation approaches. However, the training phase of the iterative sequence tagging approach uses sentences for only one round, while the inference phase is an iterative process. This makes the model focus only on the current

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Kuang, Hailan, Kewen Wu, Xiaolin Ma, and Xinhua Liu. "A Chinese Grammatical Error Correction Method Based on Iterative Training and Sequence Tagging." Applied Sciences 12, no. 9 (2022): 4364. http://dx.doi.org/10.3390/app12094364.

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Chinese grammatical error correction (GEC) is under continuous development and improvement, and this is a challenging task in the field of natural language processing due to the high complexity and flexibility of Chinese grammar. Nowadays, the iterative sequence tagging approach is widely applied to Chinese GEC tasks because it has a faster inference speed than sequence generation approaches. However, the training phase of the iterative sequence tagging approach uses sentences for only one round, while the inference phase is an iterative process. This makes the model focus only on the current

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Mahmoud, Zeinab, Chunlin Li, Marco Zappatore, et al. "Semi-supervised learning and bidirectional decoding for effective grammar correction in low-resource scenarios." PeerJ Computer Science 9 (October 24, 2023): e1639. http://dx.doi.org/10.7717/peerj-cs.1639.

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The correction of grammatical errors in natural language processing is a crucial task as it aims to enhance the accuracy and intelligibility of written language. However, developing a grammatical error correction (GEC) framework for low-resource languages presents significant challenges due to the lack of available training data. This article proposes a novel GEC framework for low-resource languages, using Arabic as a case study. To generate more training data, we propose a semi-supervised confusion method called the equal distribution of synthetic errors (EDSE), which generates a wide range o

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Wu, Zhixiao, Yao Lu, Jie Wen, and Guangming Lu. "ALRMR-GEC: Adjusting Learning Rate Based on Memory Rate to Optimize the Edit Scorer for Grammatical Error Correction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21608–16. https://doi.org/10.1609/aaai.v39i20.35464.

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Edit-based approaches for Grammatical Error Correction (GEC) have attracted volume attention due to their outstanding explanations of the correction process and rapid inference. Through exploring the characteristics of the generalized and specific knowledge learning for GEC, we discover that efficiently training GEC systems with satisfactory generalization capacity prefers more generalized knowledge rather than specific knowledge. Current gradient-based methods for training GEC systems, however, usually prioritize minimizing training loss over generalization loss. This paper proposes the strat

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Sartika, Nike, and Yuda Sukmana. "Grammatical Error Correction (GEC) of Indonesian Text Based on Neural Machine Translation (NMT)." Journal of Electrical, Electronic, Information, and Communication Technology 5, no. 2 (2023): 66. http://dx.doi.org/10.20961/jeeict.5.2.78837.

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<p class="Abstract"><span lang="EN-US">Writing errors in Indonesian are often found in various writings made in educational, government and mass media environments. The most dominant error is in spelling. This research proposes a Grammatical Error Correction (GEC) for Indonesian using the Neural Machine Translation (NMT) method, namely seq2seq, which is popularly used for English and has achieved the best performance approaching human capabilities. The model developed is made into a web-based service that is easy for users to access. The datasets used in this experiment are artific

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Fedchuk, Rostyslav, and Victoria Vysotska. "Information Technologies for Solving the Problem of Correcting Errors in Ukrainian-language Texts." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 16 (November 21, 2024): 11–34. https://doi.org/10.23939/sisn2024.16.011.

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This article is dedicated to the study and analysis of grammatical error correction (GEC) tasks in Ukrainian language texts, which is a significant issue in the field of natural language processing (NLP). The paper addresses the specific challenges faced by automatic error correction systems due to the peculiarities of the Ukrainian language, such as its morphological complexity and contextuality. Examples of typical errors are provided, and the reasons why existing GEC methods often prove insufficient for Ukrainian are analysed. The literature review covers recent research and publications in

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Tudose, Mihai-Cristian, Stefan Ruseti, and Mihai Dascalu. "Show Me All Writing Errors: A Two-Phased Grammatical Error Corrector for Romanian." Information 16, no. 3 (2025): 242. https://doi.org/10.3390/info16030242.

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Nowadays, grammatical error correction (GEC) has a significant role in writing since even native speakers often face challenges with proficient writing. This research is focused on developing a methodology to correct grammatical errors in the Romanian language, a less-resourced language for which there are currently no up-to-date GEC solutions. Our main contributions include an open-source synthetic dataset of 345,403 Romanian sentences, a manually curated dataset of 3054 social media comments, a two-phased GEC approach, and a comparison with several Romanian models, including RoMistral and Ro

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Nguyen, Phuong Thao, Bernd Nuss, Roswita Dressler, and Katie Ovens. "A Small-Scale Evaluation of Large Language Models Used for Grammatical Error Correction in a German Children’s Literature Corpus: A Comparative Study." Applied Sciences 15, no. 5 (2025): 2476. https://doi.org/10.3390/app15052476.

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Grammatical error correction (GEC) has become increasingly important for enhancing the quality of OCR-scanned texts. This small-scale study explores the application of Large Language Models (LLMs) for GEC in German children’s literature, a genre with unique linguistic challenges due to modified language, colloquial expressions, and complex layouts that often lead to OCR-induced errors. While conventional rule-based and statistical approaches have been used in the past, advancements in machine learning and artificial intelligence have introduced models capable of more contextually nuanced corre

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Qin, Mengyang. "A study on automatic correction of English grammar errors based on deep learning." Journal of Intelligent Systems 31, no. 1 (2022): 672–80. http://dx.doi.org/10.1515/jisys-2022-0052.

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Abstract Grammatical error correction (GEC) is an important element in language learning. In this article, based on deep learning, the application of the Transformer model in GEC was briefly introduced. Then, in order to improve the performance of the model on GEC, it was optimized by a generative adversarial network (GAN). Experiments were conducted on two data sets. It was found that the performance of the GAN-combined Transformer model was significantly improved compared to the Transformer model. The F 0.5 value of the optimized model was 53.87 on CoNIL-2014, which was 2.69 larger than the

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Alrehili, Ahlam, and Areej Alhothali. "Tibyan corpus: balanced and comprehensive error coverage corpus using ChatGPT for Arabic grammatical error correction." PeerJ Computer Science 11 (March 31, 2025): e2724. https://doi.org/10.7717/peerj-cs.2724.

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Natural language processing (NLP) augments text data to overcome sample size constraints. Scarce and low-quality data present particular challenges when learning from these domains. Increasing the sample size is a natural and widely used strategy for alleviating these challenges. Moreover, data-augmentation techniques are commonly used in languages with rich data resources to address problems such as exposure bias. In this study, we chose Arabic to increase the sample size and correct grammatical errors. Arabic is considered one of the languages with limited resources for grammatical error cor

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Alisoy, Hasan. "Training a Bespoke Grammatical Error Correction Model for Azerbaijani EFL Learners: A Low-Resource NLP Innovation for Educational Enhancement." EuroGlobal Journal of Linguistics and Language Education 2, no. 4 (2025): 17–24. https://doi.org/10.69760/egjlle.2504002.

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This paper presents a custom-trained grammatical error correction (GEC) system tailored to the specific L1 interference patterns of Azerbaijani English-as-Foreign-Language (EFL) learners. By collecting and annotating 3,000 learner sentences (incorrect-correct pairs) and fine-tuning a modern large language model (LLM), we demonstrate that a localized GEC model outperforms off-the-shelf tools like Grammarly and GPT-3.5. The custom model achieved a precision of 0.78 and F₀.₅ score of 0.74, compared to 0.59 for Grammarly and 0.68 for GPT-3.5 (Table 1). Notably, it corrected errors in articles, pre

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Wang, Bo, Kaoru Hirota, Chang Liu, Yaping Dai, and Zhiyang Jia. "An Approach to NMT Re-Ranking Using Sequence-Labeling for Grammatical Error Correction." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 4 (2020): 557–67. http://dx.doi.org/10.20965/jaciii.2020.p0557.

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An approach to N-best hypotheses re-ranking using a sequence-labeling model is applied to resolve the data deficiency problem in Grammatical Error Correction (GEC). Multiple candidate sentences are generated using a Neural Machine Translation (NMT) model; thereafter, these sentences are re-ranked via a stacked Transformer following a Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Field (CRF). Correlations within the sentences are extracted using the sequence-labeling model based on the Transformer, which is particularly suitable for long sentences. Meanwhile, the knowled

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Wang, Zhici, Qiancheng Yu, Jinyun Wang, Zhiyong Hu, and Aoqiang Wang. "Grammar Correction for Multiple Errors in Chinese Based on Prompt Templates." Applied Sciences 13, no. 15 (2023): 8858. http://dx.doi.org/10.3390/app13158858.

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Grammar error correction (GEC) is a crucial task in the field of Natural Language Processing (NLP). Its objective is to automatically detect and rectify grammatical mistakes in sentences, which possesses immense application research value. Currently, mainstream grammar-correction methods primarily rely on sequence labeling and text generation, which are two kinds of end-to-end methods. These methods have shown exemplary performance in areas with low error density but often fail to deliver satisfactory results in high-error density situations where multiple errors exist in a single sentence. Co

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Wang, Xiuhua, and Weixuan Zhong. "Research and Implementation of English Grammar Check and Error Correction Based on Deep Learning." Scientific Programming 2022 (January 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/4082082.

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English as a universal language in the world will get more and more attention, but English is not our mother tongue, and there exist differences in culture and thinking. English grammar is the most difficult problem to solve. There are many English learners, and the number of English teachers is limited, and it is inevitable to use Internet technology to solve the problem of lack of resources. The article uses deep learning technology to propose an ASS grammar detection model, which can quickly and efficiently detect grammatical errors. The research results show the following. (1) This study s

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Starchenko, Vladimir Mironovitch, and Alexei Mironovitch Starchenko. "Here We Go Again: Modern GEC Models Need Help with Spelling." Proceedings of the Institute for System Programming of the RAS 35, no. 5 (2023): 215–28. http://dx.doi.org/10.15514/ispras-2022-35(5)-14.

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The study focuses on how modern GEC systems handle character-level errors. We discuss the ways these errors effect the performance of models and test how models of different architectures handle them. We conclude that specialized GEC systems do struggle against correcting non-existent words, and that a simple spellchecker considerably improve overall performance of a model. To evaluate it, we assess the models over several datasets. In addition to CoNLL-2014 validation dataset, we contribute a synthetic dataset with higher density of character-level errors and conclude that, provided that mode

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Zheng, Kai, Lingmin Tan, Kezhong Liu, Mozi Chen, and Xuming Zeng. "Assessing the Performance of Multipath Mitigation for Multi-GNSS Precise Point Positioning Ambiguity Resolution." Remote Sensing 15, no. 17 (2023): 4137. http://dx.doi.org/10.3390/rs15174137.

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Real-time GNSS PPP is commonly used for high-precision positioning, but its utility is constrained by factors that necessitate extended convergence periods for a dependable accuracy. Multipath, as an unmodeled error, significantly curtails PPP performance in time-constrained scenarios. Approximately 31 consecutive days of multi-GNSS data from the satellite positioning service of the German national survey (SAPOS) network were collected to evaluate the effectiveness of multipath correction for real-time PPP ambiguity resolution (AR). Using principal component analysis (PCA) to extract the commo

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Mita, Masato. "Do Grammatical Error Correction Models Realize Grammatical Generalization?" Journal of Natural Language Processing 28, no. 4 (2021): 1331–35. http://dx.doi.org/10.5715/jnlp.28.1331.

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Zhu, Jianbin, Xiaojun Shi, and Shuanghua Zhang. "Machine Learning-Based Grammar Error Detection Method in English Composition." Scientific Programming 2021 (December 18, 2021): 1–10. http://dx.doi.org/10.1155/2021/4213791.

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The detection of grammatical errors in English composition is an important task in the field of NLP. The main purpose of this task is to check out grammatical errors in English sentences and correct them. Grammatical error detection and correction are important applications in the automatic proofreading of English texts and in the field of English learning aids. With the increasing influence of English on a global scale, a huge breakthrough has been made in the task of detecting English grammatical errors. Based on machine learning, this paper designs a new method for detecting grammatical err

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Wang, Juan, and Feng Gu. "An Automatic Error Correction Method for English Composition Grammar Based on Multilayer Perceptron." Mathematical Problems in Engineering 2022 (June 16, 2022): 1–7. http://dx.doi.org/10.1155/2022/6070445.

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In order to improve the timeliness of English grammar error correction and the recall rate of English grammar error correction, this paper proposes an automatic error correction method for English composition grammar based on a multilayer perceptron. On the basis of preprocessing the English composition corpus data, this paper extracts the grammatical features in the English composition corpus and constructs a grammatical feature set. We take the feature set as the input information of the multilayer perceptron and realize feature classification through network learning and training. The gramm

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Nowbakht, Mohammad, and Thierry Olive. "The Role of Error Type and Working Memory in Written Corrective Feedback Effectiveness on First-Language Self Error-Correction." Written Communication 38, no. 2 (2021): 278–310. http://dx.doi.org/10.1177/0741088320986554.

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This study examined the role of error-type and working memory (WM) in the effectiveness of direct-metalinguistic and indirect written corrective feedback (WCF) on self error-correction in first-language writing. Fifty-one French first-year psychology students volunteered to participate in the experiment. They carried out a first-language error-correction task after receiving WCF on typographical, orthographic, grammatical, and semantic errors. Results indicated that error-type affected the efficacy of WCF. In both groups, typographical error-correction was performed better than the others; ort

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Long, Manli, Yan Wang, Yifei Peng, and Wanwu Huang. "A Review of the Research on the Evaluation Metrics for Automatic Grammatical Error Correction System." Mobile Information Systems 2022 (October 4, 2022): 1–8. http://dx.doi.org/10.1155/2022/5998948.

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The evaluation of an automatic grammatical error correction system is an important content in the field of automatic grammatical error correction. This paper summarizes the technical routes of the four most representative evaluation metrics of the automatic grammatical error correction systems. Firstly, it introduces the characteristics and composition of each metric, then summarizes its defects, and finally puts forward some suggestions for the future development of the metrics. This paper holds that the application of natural language processing technology should be strengthened in the futur

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Pająk, Krzysztof, and Dominik Pająk. "Multilingual fine-tuning for Grammatical Error Correction." Expert Systems with Applications 200 (August 2022): 116948. http://dx.doi.org/10.1016/j.eswa.2022.116948.

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Zhang, Ying, Hidetaka Kamigaito, and Manabu Okumura. "Bidirectional Transformer Reranker for Grammatical Error Correction." Journal of Natural Language Processing 31, no. 1 (2024): 3–46. http://dx.doi.org/10.5715/jnlp.31.3.

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Duan, Ruixue, Zhiyuan Ma, Yangsen Zhang, Zhigang Ding, and Xiulei Liu. "Dynamic Assessment-Based Curriculum Learning Method for Chinese Grammatical Error Correction." Electronics 13, no. 20 (2024): 4079. http://dx.doi.org/10.3390/electronics13204079.

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Current mainstream for Chinese grammatical error correction methods rely on deep neural network models, which require a large amount of high-quality data for training. However, existing Chinese grammatical error correction corpora have a low annotation quality and high noise levels, leading to a low generalization ability of the models and difficulty in handling complex sentences. To address this issue, this paper proposes a dynamic assessment-based curriculum learning method for Chinese grammatical error correction. The proposed approach focuses on two key components: defining the difficulty

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Hongli, Chen. "Design and Application of English Grammar Error Correction System Based on Deep Learning." Security and Communication Networks 2021 (November 23, 2021): 1–9. http://dx.doi.org/10.1155/2021/4920461.

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In order to solve the problems of low correction accuracy and long correction time in the traditional English grammar error correction system, an English grammar error correction system based on deep learning is designed in this paper. This method analyzes the business requirements and functions of the English grammar error correction system and then designs the overall architecture of the system according to the analysis results, including English grammar error correction module, service access module, and feedback filtering module. The multilayer feedforward neural network is used to constru

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Mita, Masato, Tomoya Mizumoto, Masahiro Kaneko, Ryo Nagata, and Kentaro Inui. "Cross-Sectional Evaluation of Grammatical Error Correction Models." Journal of Natural Language Processing 28, no. 1 (2021): 160–82. http://dx.doi.org/10.5715/jnlp.28.160.

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Li, Zuchao, Kevin Parnow, and Hai Zhao. "Incorporating rich syntax information in Grammatical Error Correction." Information Processing & Management 59, no. 3 (2022): 102891. http://dx.doi.org/10.1016/j.ipm.2022.102891.

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Zeng, Guanguan. "Intelligent Test Algorithm for English Writing Using English Semantic and Neural Networks." Mobile Information Systems 2022 (June 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/4484201.

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English writing is considered by English learners to be the portion of English learning with the greatest application, the most thorough understanding, and the most challenging instruction. It automatically detects and corrects (DAC) grammatical faults in English writing, which is critical in the English learning and teaching processes. The goal of this research is to investigate the sequence annotation model and the Seq2Seq NN model based on cyclic NN, and to use these two models to detect grammatical faults in English (EGE). This paper provides an EGE DAC approach based on sequence annotatio

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