Youdao’s Winning Solution to the NLPCC-2018 Task 2 Challenge: A Neural Machine Translation Approach to Chinese Grammatical Error Correction

Ke, Fu; Huang, Jin; Duan, Yitao

doi:10.1007/978-3-319-99495-6_29

Cited by 32 publications

(28 citation statements)

References 10 publications

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“…Systems for Chinese GEC also rely on sequence to sequence models. The NLPCC 2018 shared task winner uses five different models in tandem, and chooses the best output with a 5-gram language model (Fu et al, 2018). Ren et al (2018) use an ensemble of Convolutional sequence to sequence models with pre-trained word embeddings.…”

Section: Nmt-based Methodsmentioning

confidence: 99%

Heterogeneous Recycle Generation for Chinese Grammatical Error Correction

Hinson¹,

Huang²,

Chen³

2020

Proceedings of the 28th International Conference on Computational Linguistics

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Most recent works in the field of grammatical error correction (GEC) rely on neural machine translation-based models. Although these models boast impressive performance, they require a massive amount of data to properly train. Furthermore, NMT-based systems treat GEC purely as a translation task and overlook the editing aspect of it. In this work we propose a heterogeneous approach to Chinese GEC, composed of a NMT-based model, a sequence editing model, and a spell checker. Our methodology not only achieves a new state-of-the-art performance for Chinese GEC, but also does so without relying on data augmentation or GEC-specific architecture changes. We further experiment with all possible configurations of our system with respect to model composition order and number of rounds of correction. A detailed analysis of each model and their contributions to the correction process is performed by adapting the ERRANT scorer to be able to score Chinese sentences.

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Section: Nmt-based Methodsmentioning

confidence: 99%

Heterogeneous Recycle Generation for Chinese Grammatical Error Correction

Hinson¹,

Huang²,

Chen³

2020

Proceedings of the 28th International Conference on Computational Linguistics

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“…Grammar error detection and correction has been done for various languages [14], including Chinese [15], Greek [16], and Swedish [17]. Different approaches have been exploited for automatically detecting and correcting text.…”

Section: Grammar Detection and Correction Approachesmentioning

confidence: 99%

Error Detection for Arabic Text Using Neural Sequence Labeling

Madi

Al‐Khalifa

2020

Applied Sciences

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The English language has, thus far, received the most attention in research concerning automatic grammar error correction and detection. However, these tasks have been less investigated for other languages. In this paper, we present the first experiments using neural network models for the task of error detection for Modern Standard Arabic (MSA) text. We investigate several neural network architectures and report the evaluation results acquired by applying cross-validation on the data. All experiments involve a corpus we created and augmented. The corpus has 494 sentences and 620 sentences after augmentation. Our models achieved a maximum precision of 78.09%, recall of 83.95%, and F0.5 score of 79.62% in the error detection task using SimpleRNN. Using an LSTM, we achieved a maximum precision of 79.21%, recall of 93.8%, and F0.5 score of 79.16%. Finally, the best results were achieved using a BiLSTM with a maximum precision of 80.74%, recall of 85.73%, and F0.5 score of 81.55%. We compared the results of the three models to a baseline, which is a commercially available Arabic grammar checker (Microsoft Word 2007). LSTM, BiLSTM, and SimpleRNN all outperformed the baseline in precision and F0.5. Our work shows preliminary results, demonstrating that neural network architectures for error detection through sequence labeling can successfully be applied to Arabic text.

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“…GEC models contained rule-based model, NMT model and SMT model. Fu et al [31] regarded the CGEC task as a translation problem which translated the wrong sentence into the correct one. Fu et al [32] built the detection model through bidirectional Long Short-Term Memory with a conditional random field layer (BiLSTM-CRF) and the correction model based on the ePMI values and seq2seq model.…”

Section: Related Workmentioning

confidence: 99%

Chinese Grammatical Error Correction Based on Convolutional Sequence to Sequence Model

Zhao

Shi

et al. 2019

IEEE Access

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Chinese grammatical error correction (CGEC) is practically useful for learners of Chinese as a second language, but it is a rather challenging task due to the complex and flexible nature of Chinese language so that existing methods for English cannot be directly applied. In this paper, we introduce a convolutional sequence to sequence model into the CGEC task for the first time, since many Chinese grammatical errors are concentrated between three and four words and convolutional neural network can better capture the local context. A convolution-based model can obtain the representations of the context by fixed size kernel. By stacking convolution layers, long-term dependences can be obtained. We also propose two optimization methods, shared embedding and policy gradient, to optimize the convolutional sequence to sequence model through sharing parameters and reconstructing loss function. Besides, we collate the existing Chinese grammatical correction corpus in detail. The results show that the models we proposed two different optimization methods both achieve large improvement compared with the natural machine translation model based on a recurrent neural network.

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Youdao’s Winning Solution to the NLPCC-2018 Task 2 Challenge: A Neural Machine Translation Approach to Chinese Grammatical Error Correction

Cited by 32 publications

References 10 publications

Heterogeneous Recycle Generation for Chinese Grammatical Error Correction

Heterogeneous Recycle Generation for Chinese Grammatical Error Correction

Error Detection for Arabic Text Using Neural Sequence Labeling

Chinese Grammatical Error Correction Based on Convolutional Sequence to Sequence Model

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