Results 21 to 30 of about 58,487 (271)
An Automatic Error Detection Method for Machine Translation Results via Deep Learning
IEEE Access, 2023 Nowadays, the rapid development of natural language processing has brought great progress for the area of machine translation. Various deep neural network-based machine translation approaches have been more and more general.
Weihong Zhang
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Spoken Language ‘Grammatical Error Correction’ [PDF]
Interspeech 2020, 2020 Spoken language ‘grammatical error correction’ (GEC) is an important mechanism to help learners of a foreign language, here English, improve their spoken grammar. GEC is challeng- ing for non-native spoken language due to interruptions from disfluent speech events such as repetitions and false starts and issues in strictly defining what is acceptable ...
Lu, Yiting, Gales, Mark JF, Wang, Yu
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Towards Lithuanian Grammatical Error Correction
, 2022 Everyone wants to write beautiful and correct text, yet the lack of language skills, experience, or hasty typing can result in errors. By employing the recent advances in transformer architectures, we construct a grammatical error correction model for Lithuanian, the language rich in archaic features.
Lukas Stankevičius +1 more
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IEEE Access, 2023 Grammatical error correction aims to detect and correct grammatical errors with all types of mistaken, disordered, missing, and redundant characters. However, most existing methods focus more on detecting errors than correcting them.
Yin Wang, Zhenghan Chen
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Crowdsourcing for grammatical error correction [PDF]
Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing, 2014 We discuss the problem of grammatical error correction, which has gained attention for its usefulness both in the development of tools for learners of foreign languages and as a component of statistical machine translation systems. We believe the task of suggesting grammar and style corrections in writing is well suited to a crowdsourcing solution but ...
Ellie Pavlick +2 more
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Grammatical Error Correction with Denoising Autoencoder [PDF]
International Journal of Advanced Computer Science and Applications, 2021 A denoising autoencoder sequence-to-sequence model based on transformer architecture proved to be useful for underlying tasks such as summarization, machine translation, or question answering. This paper investigates the possibilities of using this model type for grammatical error correction and introduces a novel method of remark-based model ...
Krzysztof Pajak, Adam Gonczarek
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Method for Chinese Grammar Error Detection Integrating ELECTRA and Text Local Information [PDF]
Jisuanji gongcheng, 2023 Grammar error detection is a basic task in natural language processing.The task aims to automatically identify typos, grammar, and word order errors in text.Compared with other languages, Chinese grammar is flexible and lacks symbolic information such as
CHEN Bailin, WANG Tianji, REN Lina, HUANG Ruizhang
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Cross-Sentence Grammatical Error Correction [PDF]
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019 Automatic grammatical error correction (GEC) research has made remarkable progress in the past decade. However, all existing approaches to GEC correct errors by considering a single sentence alone and ignoring crucial cross-sentence context. Some errors can only be corrected reliably using cross-sentence context and models can also benefit from the ...
Shamil Chollampatt +2 more
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Semi-supervised learning and bidirectional decoding for effective grammar correction in low-resource scenarios [PDF]
PeerJ Computer Science, 2023 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.
Zeinab Mahmoud +6 more
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Controllable data synthesis method for grammatical error correction [PDF]
Frontiers of Computer Science, 2021 Due to the lack of parallel data in current Grammatical Error Correction (GEC) task, models based on Sequence to Sequence framework cannot be adequately trained to obtain higher performance. We propose two data synthesis methods which can control the error rate and the ratio of error types on synthetic data.
Yang, Liner +4 more
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