A Unified Framework for Grammar Error Correction

Zhang, Longkai; Wang, Houfeng

doi:10.3115/v1/w14-1713

Cited by 6 publications

(7 citation statements)

References 3 publications

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“…Earlier studies performed well in spelling and grammatical error correction with relatively basic approaches involving ngram [2,3] confusion set [4,5], statistical machine translation architecture [6,[16][17][18]. Generally, these previously mentioned methods are employed as compounds more frequently than individually.…”

Section: Related Workmentioning

confidence: 99%

“…Prior GEC and GED studies have obtained the outstanding achievements in this area. Most of them employed n-gram [2,3], confusion set [4,5], language model [6] including the BERT [7][8][9] etc. to diagnose the errors.…”

Section: Introductionmentioning

confidence: 99%

“…Encoder-decoder [10,11], a novel approach, has reached the new achievements in GEC [11,12] as in machine translation. Notably, most of the recent advanced GEC architectures utilize the advantages of multiple models to solve different sort of problems [3,5,13], involving Transformer-based methods [14,15]. (There are two omissions of the particle indicating the durative stative "zhe".…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Zhang

2022

IEEE Access

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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 deaf 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 encoderdecoder architecture based on multi-head self-attention along with multiple strategies, which excels at extracting deep representations from the corrupted sentences of deaf 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 deaf 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 deaf students' grammatical error correction or in a common grammatical error correction shared task.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chen

Zhang

2022

IEEE Access

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“…Note that Deep Spelling is essentially not a spelling corrector since spelling correction must focus only on the misspelled words, not on transforming the whole sentences. For similar reasons, spelling correction is also different from GEC (Grammar Error Correction) (Zhang and Wang, 2014;Junczys-Dowmunt et al, 2018).…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Context-aware Stand-alone Neural Spelling Correction

Li¹,

Liu

Huang

2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. On the contrary, humans can easily infer the corresponding correct words from their misspellings and surrounding context. Inspired by this, we address the standalone spelling correction problem, which only corrects the spelling of each token without additional token insertion or deletion, by utilizing both spelling information and global context representations. We present a simple yet powerful solution that jointly detects and corrects misspellings as a sequence labeling task by fine-turning a pre-trained language model. Our solution outperform the previous state-ofthe-art result by 12.8% absolute F 0.5 score.

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“…In classifier-based approaches such as in Rozovskaya et al (2013), Rozovskaya et al (2014), Zhang and Wang (2014), Wang et al (2014), andTomeh et al (2014), feature extraction was an inescapable step, which is time-consuming and needs to be handled cautiously. Also, many works created separate classifiers for each error type which is useful only when the errors are not dependent on each other as it is unable to solve the problem of dependent errors.…”

Section: Hypernymmentioning

confidence: 99%

Grammar checking and relation extraction in text: approaches, techniques and open challenges

Madi

Al-Matham

Al‐Khalifa

2019

DTA

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Purpose The purpose of this paper is to provide an overall review of grammar checking and relation extraction (RE) literature, their techniques and the open challenges associated with them; and, finally, suggest future directions. Design/methodology/approach The review on grammar checking and RE was carried out using the following protocol: we prepared research questions, planed for searching strategy, addressed paper selection criteria to distinguish relevant works, extracted data from these works, and finally, analyzed and synthesized the data. Findings The output of error detection models could be used for creating a profile of a certain writer. Such profiles can be used for author identification, native language identification or even the level of education, to name a few. The automatic extraction of relations could be used to build or complete electronic lexical thesauri and knowledge bases. Originality/value Grammar checking is the process of detecting and sometimes correcting erroneous words in the text, while RE is the process of detecting and categorizing predefined relationships between entities or words that were identified in the text. The authors found that the most obvious challenge is the lack of data sets, especially for low-resource languages. Also, the lack of unified evaluation methods hinders the ability to compare results.

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A Unified Framework for Grammar Error Correction

Cited by 6 publications

References 3 publications

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

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

Context-aware Stand-alone Neural Spelling Correction

Grammar checking and relation extraction in text: approaches, techniques and open challenges

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