Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students

Grammatical error correction has been considered as an application closely related to daily life and an important shared task in many prestigious competitions and workshops. The neural machine translation with an encoder-decoder architecture containing language models has been the fundamental soluti...
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Gespeichert in:

Autor*in:	Binbin Chen [verfasserIn] Jingyu Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Hearing impaired student grammatical error correction self-attention encoder-decoder pre-training

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 35061-35072
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:35061-35072

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3159676

Katalog-ID:	DOAJ051008432

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spelling	10.1109/ACCESS.2022.3159676 doi (DE-627)DOAJ051008432 (DE-599)DOAJf91ddc0c799e4e43a121375c803c720a DE-627 ger DE-627 rakwb eng TK1-9971 Binbin Chen verfasserin aut Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Grammatical error correction has been considered as an application closely related to daily life and an important shared task in many prestigious competitions and workshops. The neural machine translation with an encoder-decoder architecture containing language models has been the fundamental solution for the grammatical error correction. Whereas Grammatical error correction task on texts of hearing impaired people or its solution has not been seen yet, and common Grammatical error correction tasks are suffering several challenges, such as insufficient training data, insufficient accuracy due to the unsatisfactory capacity of extracting semantic and grammatical patterns. Under these circumstances, we proposed a novel encoder-decoder architecture based on multi-head self-attention along with multiple strategies, which excels at extracting deep representations from the corrupted sentences of hearing impaired students and further reconstructing the sentences into grammatical ones. Via the re-ranking strategy, our model can correct various kinds of errors including spelling and complex syntax errors. The ablation experiments prove that the semantic extracting of self-attention mechanism excluding the position encoding with the word order shuffle operation can significantly learn the hearing impaired students’ sentence patterns whose word order is quite different from the ones of hearing people and improve the correction scores. The pre-training can enhance the restoring efficiency of sentence structure in the decoding process. The comparison experiments with baseline models show that our model obtains superior performance either in the hearing impaired students’ grammatical error correction or in a common grammatical error correction shared task. Hearing impaired student grammatical error correction self-attention encoder-decoder pre-training Electrical engineering. Electronics. Nuclear engineering Jingyu Zhang verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 35061-35072 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:35061-35072 https://doi.org/10.1109/ACCESS.2022.3159676 kostenfrei https://doaj.org/article/f91ddc0c799e4e43a121375c803c720a kostenfrei https://ieeexplore.ieee.org/document/9734023/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 35061-35072
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allfieldsGer	10.1109/ACCESS.2022.3159676 doi (DE-627)DOAJ051008432 (DE-599)DOAJf91ddc0c799e4e43a121375c803c720a DE-627 ger DE-627 rakwb eng TK1-9971 Binbin Chen verfasserin aut Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Grammatical error correction has been considered as an application closely related to daily life and an important shared task in many prestigious competitions and workshops. The neural machine translation with an encoder-decoder architecture containing language models has been the fundamental solution for the grammatical error correction. Whereas Grammatical error correction task on texts of hearing impaired people or its solution has not been seen yet, and common Grammatical error correction tasks are suffering several challenges, such as insufficient training data, insufficient accuracy due to the unsatisfactory capacity of extracting semantic and grammatical patterns. Under these circumstances, we proposed a novel encoder-decoder architecture based on multi-head self-attention along with multiple strategies, which excels at extracting deep representations from the corrupted sentences of hearing impaired students and further reconstructing the sentences into grammatical ones. Via the re-ranking strategy, our model can correct various kinds of errors including spelling and complex syntax errors. The ablation experiments prove that the semantic extracting of self-attention mechanism excluding the position encoding with the word order shuffle operation can significantly learn the hearing impaired students’ sentence patterns whose word order is quite different from the ones of hearing people and improve the correction scores. The pre-training can enhance the restoring efficiency of sentence structure in the decoding process. The comparison experiments with baseline models show that our model obtains superior performance either in the hearing impaired students’ grammatical error correction or in a common grammatical error correction shared task. Hearing impaired student grammatical error correction self-attention encoder-decoder pre-training Electrical engineering. Electronics. Nuclear engineering Jingyu Zhang verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 35061-35072 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:35061-35072 https://doi.org/10.1109/ACCESS.2022.3159676 kostenfrei https://doaj.org/article/f91ddc0c799e4e43a121375c803c720a kostenfrei https://ieeexplore.ieee.org/document/9734023/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 35061-35072
allfieldsSound	10.1109/ACCESS.2022.3159676 doi (DE-627)DOAJ051008432 (DE-599)DOAJf91ddc0c799e4e43a121375c803c720a DE-627 ger DE-627 rakwb eng TK1-9971 Binbin Chen verfasserin aut Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Grammatical error correction has been considered as an application closely related to daily life and an important shared task in many prestigious competitions and workshops. The neural machine translation with an encoder-decoder architecture containing language models has been the fundamental solution for the grammatical error correction. Whereas Grammatical error correction task on texts of hearing impaired people or its solution has not been seen yet, and common Grammatical error correction tasks are suffering several challenges, such as insufficient training data, insufficient accuracy due to the unsatisfactory capacity of extracting semantic and grammatical patterns. Under these circumstances, we proposed a novel encoder-decoder architecture based on multi-head self-attention along with multiple strategies, which excels at extracting deep representations from the corrupted sentences of hearing impaired students and further reconstructing the sentences into grammatical ones. Via the re-ranking strategy, our model can correct various kinds of errors including spelling and complex syntax errors. The ablation experiments prove that the semantic extracting of self-attention mechanism excluding the position encoding with the word order shuffle operation can significantly learn the hearing impaired students’ sentence patterns whose word order is quite different from the ones of hearing people and improve the correction scores. The pre-training can enhance the restoring efficiency of sentence structure in the decoding process. The comparison experiments with baseline models show that our model obtains superior performance either in the hearing impaired students’ grammatical error correction or in a common grammatical error correction shared task. Hearing impaired student grammatical error correction self-attention encoder-decoder pre-training Electrical engineering. Electronics. Nuclear engineering Jingyu Zhang verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 35061-35072 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:35061-35072 https://doi.org/10.1109/ACCESS.2022.3159676 kostenfrei https://doaj.org/article/f91ddc0c799e4e43a121375c803c720a kostenfrei https://ieeexplore.ieee.org/document/9734023/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 35061-35072
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callnumber-first	T - Technology
author	Binbin Chen
spellingShingle	Binbin Chen misc TK1-9971 misc Hearing impaired student misc grammatical error correction misc self-attention misc encoder-decoder misc pre-training misc Electrical engineering. Electronics. Nuclear engineering Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students
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topic_title	TK1-9971 Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students Hearing impaired student grammatical error correction self-attention encoder-decoder pre-training
topic	misc TK1-9971 misc Hearing impaired student misc grammatical error correction misc self-attention misc encoder-decoder misc pre-training misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Hearing impaired student misc grammatical error correction misc self-attention misc encoder-decoder misc pre-training misc Electrical engineering. Electronics. Nuclear engineering
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title	Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students
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title_full	Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students
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title_sort	pre-training-based grammatical error correction model for the written language of chinese hearing impaired students
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title_auth	Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students
abstract	Grammatical error correction has been considered as an application closely related to daily life and an important shared task in many prestigious competitions and workshops. The neural machine translation with an encoder-decoder architecture containing language models has been the fundamental solution for the grammatical error correction. Whereas Grammatical error correction task on texts of hearing impaired people or its solution has not been seen yet, and common Grammatical error correction tasks are suffering several challenges, such as insufficient training data, insufficient accuracy due to the unsatisfactory capacity of extracting semantic and grammatical patterns. Under these circumstances, we proposed a novel encoder-decoder architecture based on multi-head self-attention along with multiple strategies, which excels at extracting deep representations from the corrupted sentences of hearing impaired students and further reconstructing the sentences into grammatical ones. Via the re-ranking strategy, our model can correct various kinds of errors including spelling and complex syntax errors. The ablation experiments prove that the semantic extracting of self-attention mechanism excluding the position encoding with the word order shuffle operation can significantly learn the hearing impaired students’ sentence patterns whose word order is quite different from the ones of hearing people and improve the correction scores. The pre-training can enhance the restoring efficiency of sentence structure in the decoding process. The comparison experiments with baseline models show that our model obtains superior performance either in the hearing impaired students’ grammatical error correction or in a common grammatical error correction shared task.
abstractGer	Grammatical error correction has been considered as an application closely related to daily life and an important shared task in many prestigious competitions and workshops. The neural machine translation with an encoder-decoder architecture containing language models has been the fundamental solution for the grammatical error correction. Whereas Grammatical error correction task on texts of hearing impaired people or its solution has not been seen yet, and common Grammatical error correction tasks are suffering several challenges, such as insufficient training data, insufficient accuracy due to the unsatisfactory capacity of extracting semantic and grammatical patterns. Under these circumstances, we proposed a novel encoder-decoder architecture based on multi-head self-attention along with multiple strategies, which excels at extracting deep representations from the corrupted sentences of hearing impaired students and further reconstructing the sentences into grammatical ones. Via the re-ranking strategy, our model can correct various kinds of errors including spelling and complex syntax errors. The ablation experiments prove that the semantic extracting of self-attention mechanism excluding the position encoding with the word order shuffle operation can significantly learn the hearing impaired students’ sentence patterns whose word order is quite different from the ones of hearing people and improve the correction scores. The pre-training can enhance the restoring efficiency of sentence structure in the decoding process. The comparison experiments with baseline models show that our model obtains superior performance either in the hearing impaired students’ grammatical error correction or in a common grammatical error correction shared task.
abstract_unstemmed	Grammatical error correction has been considered as an application closely related to daily life and an important shared task in many prestigious competitions and workshops. The neural machine translation with an encoder-decoder architecture containing language models has been the fundamental solution for the grammatical error correction. Whereas Grammatical error correction task on texts of hearing impaired people or its solution has not been seen yet, and common Grammatical error correction tasks are suffering several challenges, such as insufficient training data, insufficient accuracy due to the unsatisfactory capacity of extracting semantic and grammatical patterns. Under these circumstances, we proposed a novel encoder-decoder architecture based on multi-head self-attention along with multiple strategies, which excels at extracting deep representations from the corrupted sentences of hearing impaired students and further reconstructing the sentences into grammatical ones. Via the re-ranking strategy, our model can correct various kinds of errors including spelling and complex syntax errors. The ablation experiments prove that the semantic extracting of self-attention mechanism excluding the position encoding with the word order shuffle operation can significantly learn the hearing impaired students’ sentence patterns whose word order is quite different from the ones of hearing people and improve the correction scores. The pre-training can enhance the restoring efficiency of sentence structure in the decoding process. The comparison experiments with baseline models show that our model obtains superior performance either in the hearing impaired students’ grammatical error correction or in a common grammatical error correction shared task.
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title_short	Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students
url	https://doi.org/10.1109/ACCESS.2022.3159676 https://doaj.org/article/f91ddc0c799e4e43a121375c803c720a https://ieeexplore.ieee.org/document/9734023/ https://doaj.org/toc/2169-3536
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up_date	2024-07-03T17:56:28.548Z
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