The Construction of Machine Translation Model and Its Application in English Grammar Error Detection
In order to solve the problems of low accuracy, recall rate, and F1 value of traditional English grammar error detection methods, a new machine translation model is constructed and applied to English grammar error detection. In the encoder-decoder framework, the machine translation model is construc...
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| Vydané v: | Security and communication networks Ročník 2021; s. 1 - 11 |
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| Médium: | Journal Article |
| Jazyk: | English |
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London
Hindawi
18.12.2021
John Wiley & Sons, Inc |
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| ISSN: | 1939-0114, 1939-0122 |
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| Abstract | In order to solve the problems of low accuracy, recall rate, and F1 value of traditional English grammar error detection methods, a new machine translation model is constructed and applied to English grammar error detection. In the encoder-decoder framework, the machine translation model is constructed through the steps of word vector generation, encoder language model construction, decoder language model construction, word alignment, output module, and so on. On this basis, the machine translation model is trained to detect English grammatical errors through dependency analysis and alternative word generation. Experimental results show that the accuracy, recall rate, and F1 value of the proposed method are higher than those of the experimental comparison method for detecting English grammatical errors such as articles, prepositions, nouns, verbs, and subject-verb agreement, indicating that the proposed method is of high practical value. |
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| AbstractList | In order to solve the problems of low accuracy, recall rate, and F1 value of traditional English grammar error detection methods, a new machine translation model is constructed and applied to English grammar error detection. In the encoder-decoder framework, the machine translation model is constructed through the steps of word vector generation, encoder language model construction, decoder language model construction, word alignment, output module, and so on. On this basis, the machine translation model is trained to detect English grammatical errors through dependency analysis and alternative word generation. Experimental results show that the accuracy, recall rate, and F1 value of the proposed method are higher than those of the experimental comparison method for detecting English grammatical errors such as articles, prepositions, nouns, verbs, and subject-verb agreement, indicating that the proposed method is of high practical value. |
| Author | Long, Fei |
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| CitedBy_id | crossref_primary_10_1155_2022_4472190 crossref_primary_10_1016_j_entcom_2024_100789 crossref_primary_10_1155_2023_9857543 crossref_primary_10_1155_2022_6330814 |
| Cites_doi | 10.1016/s0042-6989(97)00273-3 10.1111/coin.12405 10.1016/s2152-2650(20)30965-4 10.1109/LSP.2021.3058896 10.11992/tis.202001014 10.1016/J.PATCOG.2021.108273 10.1016/j.joto.2019.09.005 10.1007/978-3-030-58601-0_2 10.1109/ACCESS.2020.2985617 10.1007/s12144-021-01622-w 10.1016/j.procs.2020.12.015 10.1109/access.2020.3009058 10.14283/jpad.2021.30 10.1016/j.measurement.2021.109094 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Fei Long. Copyright © 2021 Fei Long. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | Coders Communication Encoders-Decoders English language Error correction & detection Error detection Grammar Language Machine translation Natural language Neural networks Recall Syntax Translations |
| Title | The Construction of Machine Translation Model and Its Application in English Grammar Error Detection |
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