A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction

Mita, Masato; Kiyono, Satoshi; Kaneko, Masahiro; Suzuki, Jun; Inui, Kentaro

doi:10.18653/v1/2020.findings-emnlp.26

Cited by 9 publications

(7 citation statements)

References 39 publications

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“…A common problem in GEC is that the largest publicly-available high-quality parallel corpora only contain roughly 50k sentence pairs, and larger corpora, such as Lang-8, are noisy (Mita et al 2020;Rothe et al 2021). This data sparsity problem has motivated a lot of research into synthetic data generation, especially in the context of resourceheavy NMT approaches, because synthetic data primarily requires a native monolingual source corpus rather than a labour-intensive manual annotation process.…”

Section: Data Augmentationmentioning

confidence: 99%

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Grammatical Error Correction: A Survey of the State of the Art

Bryant¹,

Zheng²,

Qorib³

et al. 2022

Preprint

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Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject-verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarise the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgements, before concluding with an overview of recent progress and suggestions for future work and remaining challenges. We hope that this survey will serve as comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.

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Section: Data Augmentationmentioning

confidence: 99%

“…One direction focuses on correcting noisy sentences. Mita et al (2020) and Rothe et al (2021) achieve this by incorporating a well-trained GEC model to reduce wrong corrections. The other direction attempts to down-weight noisy sentences.…”

Section: Augmenting Official Datasetsmentioning

confidence: 99%

Grammatical Error Correction: A Survey of the State of the Art

Bryant¹,

Zheng²,

Qorib³

et al. 2022

Preprint

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“…Therefore, it is necessary to filter out the error-free spans. Mita et al (2020) compare the perplexities of generated sentences and correct sentences to determine whether the generated sentences are grammatically correct. However, since the sentence-level perplexity is affected by too many tokens, the sentence with larger perplexity may also be grammatically correct.…”

Section: Erroneous Sentence Constructionmentioning

confidence: 99%

Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation

Yue¹,

Liu²,

Cai³

et al. 2022

Findings of the Association for Computational Linguistics: ACL 2022

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Chinese Grammatical Error Detection(CGED) aims at detecting grammatical errors in Chinese texts. One of the main challenges for CGED is the lack of annotated data. To alleviate this problem, previous studies proposed various methods to automatically generate more training samples, which can be roughly categorized into rule-based methods and model-based methods. The rule-based methods construct erroneous sentences by directly introducing noises into original sentences. However, the introduced noises are usually context-independent, which are quite different from those made by humans. The model-based methods utilize generative models to imitate human errors. The generative model may bring too many changes to the original sentences and generate semantically ambiguous sentences, so it is difficult to detect grammatical errors in these generated sentences. In addition, generated sentences may be error-free and thus become noisy data. To handle these problems, we propose CNEG, a novel Conditional Non-Autoregressive Error Generation model for generating Chinese grammatical errors. Specifically, in order to generate a context-dependent error, we first mask a span in a correct text, then predict an erroneous span conditioned on both the masked text and the correct span. Furthermore, we filter out error-free spans by measuring their perplexities in the original sentences. Experimental results show that our proposed method achieves better performance than all compared data augmentation methods on the CGED-2018 and CGED-2020 benchmarks.

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“…Key Words: Grammatical Error Correction, Evaluation Corpus, Error Tag, Japanese 1 はじめに文法誤り訂正とは，与えられた文章中の文法誤りを文法的に正しい表現に訂正するタスクである．主に語学学習者が書いた文章を対象とし，自然言語処理の教育応用における主要タスクのひとつとなっている．これまでルールに基づく手法 (Schneider and McCoy 1998) や言語モデルに基づく手法 (Gamon et al 2008)，分類器に基づく手法 (Dahlmeier and Ng 2011) などが開発されてきた．近年では機械翻訳に基づく手法 (Brockett et al 2006) が盛んに研究されている (Chollampatt and Ng 2018;Junczys-Dowmunt et al 2018;Zhao et al 2019;Lichtarge et al 2019;Kiyono et al 2020;Kaneko et al 2020;Rothe et al 2021; Kiyono et al 2020;Kaneko et al 2020;Yasunaga et al 2021;Lai et al 2022)．具体的には CoNLL-2014 shared task 評価コーパスに加え，FCE (Yannakoudakis et al 2011) や JFLEG (Napoles et al 2017)，W&I+LOCNESS (Granger 1998;Yannakoudakis et al 2018)，GMEG (Napoles et (Xie et al 2018;Ge et al 2018a;Zhao et al 2019;Lichtarge et al 2019Lichtarge et al , 2020Kiyono et al 2020;Wang and Zheng 2020;Zhou et al 2020;Wan et al 2020;Stahlberg and Kumar 2021;Yasunaga et al 2021;…”

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Construction of an Error-Tagged Evaluation Corpus for Japanese Grammatical Error Correction

Koyama

Kiyuna

Kono

et al. 2023

Journal of Natural Language Processing

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This study constructed an error-tagged evaluation corpus for Japanese grammatical error correction (GEC). Evaluation corpora are essential for assessing the performance of models. The availability of various evaluation corpora for English GEC has facilitated a comprehensive comparison between models and the development of the English GEC community. However, the development of the Japanese GEC community has been hindered due to the lack of available evaluation corpora in the Japanese GEC. As a result, we constructed a new evaluation corpus for the Japanese GEC and made it available to the public. We used texts written by the Japanese language learners in the Lang-8 corpus, a representative learner corpus in GEC, to create the

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A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction

Cited by 9 publications

References 39 publications

Grammatical Error Correction: A Survey of the State of the Art

Grammatical Error Correction: A Survey of the State of the Art

Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation

Construction of an Error-Tagged Evaluation Corpus for Japanese Grammatical Error Correction

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