{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:38:55Z","timestamp":1762508335845,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T00:00:00Z","timestamp":1606262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007530","name":"National Taiwan University of Science and Technology","doi-asserted-by":"publisher","award":["TIT-NTUST-107-05"],"award-info":[{"award-number":["TIT-NTUST-107-05"]}],"id":[{"id":"10.13039\/501100007530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In recent years, Chinese has become one of the most popular languages globally. The demand for automatic Chinese sentence correction has gradually increased. This research can be adopted to Chinese language learning to reduce the cost of learning and feedback time, and help writers check for wrong words. The traditional way to do Chinese sentence correction is to check if the word exists in the predefined dictionary. However, this kind of method cannot deal with semantic error. As deep learning becomes popular, an artificial neural network can be applied to understand the sentence\u2019s context to correct the semantic error. However, there are still many issues that need to be discussed. For example, the accuracy and the computation time required to correct a sentence are still lacking, so maybe it is still not the time to adopt the deep learning based Chinese sentence correction system to large-scale commercial applications. Our goal is to obtain a model with better accuracy and computation time. Combining recurrent neural network and Bidirectional Encoder Representations from Transformers (BERT), a recently popular model, known for its high performance and slow inference speed, we introduce a hybrid model which can be applied to Chinese sentence correction, improving the accuracy and also the inference speed. Among the results, BERT-GRU has obtained the highest BLEU Score in all experiments. The inference speed of the transformer-based original model can be improved by 1131% in beam search decoding in the 128-word experiment, and greedy decoding can also be improved by 452%. The longer the sequence, the larger the improvement.<\/jats:p>","DOI":"10.3390\/sym12121939","type":"journal-article","created":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T21:55:06Z","timestamp":1606341306000},"page":"1939","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Applying a Hybrid Sequential Model to Chinese Sentence Correction"],"prefix":"10.3390","volume":"12","author":[{"given":"Jun Wei","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, College of Electrical Engineering and Computer Science, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xanno K.","family":"Sigalingging","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, College of Electrical Engineering and Computer Science, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7197-9912","authenticated-orcid":false,"given":"Jenq-Shiou","family":"Leu","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, College of Electrical Engineering and Computer Science, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9108-3010","authenticated-orcid":false,"given":"Jun-Ichi","family":"Takada","sequence":"additional","affiliation":[{"name":"Department of International Development Engineering, Graduate School of Science and Engineering, Tokyo Institute of Technology, 2-12-1-S6-4, O-okayama, Meguro-ku, Tokyo 152-8550, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,25]]},"reference":[{"key":"ref_1","unstructured":"Huang, C.M., Wu, M.C., and Chang, C.C. (2007, January 16\u201318). Error Detection and Correction Based on Chinese Phonemic Alphabet in Chinese Text. Proceedings of the International Conference on Modeling Decisions for Artificial Intelligence, Kitakyushu, Japan."},{"key":"ref_2","unstructured":"Shiue, Y.T., Huang, H.H., and Chen, H. (2018, January 20\u201326). Correcting Chinese Word Usage Errors for Learning Chinese as a Second Language. Proceedings of the COLING, Santa Fe, NM, USA."},{"key":"ref_3","unstructured":"Cheng, S.M., Yu, C.H., and Chen, H.H. (2014, January 23\u201329). Chinese Word Ordering Errors Detection and Correction for Non-Native Chinese Language Learners. Proceedings of the COLING 2014, the 25th International Conference on Computational Linguistics, Dublin, Ireland. Technical Papers."},{"key":"ref_4","first-page":"529","article-title":"On certain integrals of Lipschitz-Hankel type involving products of bessel functions","volume":"247","author":"Eason","year":"1955","journal-title":"Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., and Zhu, W.J. (2002, January 7\u201312). BLEU: A Method for Automatic Evaluation of Machine Translation. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, PA, USA.","DOI":"10.3115\/1073083.1073135"},{"key":"ref_6","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2016). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv."},{"key":"ref_7","unstructured":"Ge, T., Zhang, X., Wei, F., and Zhou, M. (August, January 28). Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Schmaltz, A., Kim, Y., Rush, A.M., and Shieber, S. (2016, January 16). Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction. Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, San Diego, CA, USA.","DOI":"10.18653\/v1\/W16-0528"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"72905","DOI":"10.1109\/ACCESS.2019.2917631","article-title":"Chinese Grammatical Error Correction Based on Convolutional Sequence to Sequence Model","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","unstructured":"Sherstinsky, A. (2018). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network. arXiv."},{"key":"ref_11","unstructured":"(2020, November 20). Recurrent Neural Network. Available online: https:\/\/www.cs.toronto.edu\/~tingwuwang\/rnn_tutorial.pdf."},{"key":"ref_12","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V. (2014). Sequence to Sequence Learning with Neural Networks. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_14","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv."},{"key":"ref_15","unstructured":"Kingma, D.P., and Ba, J. (2017). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Loper, E., and Bird, S. (2002). NLTK: The Natural Language Toolkit. arXiv.","DOI":"10.3115\/1118108.1118117"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Jiang, N., Sun, W., and Wan, X. (2018, January 26\u201330). Overview of the NLPCC 2018 Shared Task: Grammatical Error Correction. Proceedings of the 7th CCF International Conference, NLPCC 2018, Hohhot, China.","DOI":"10.1007\/978-3-319-99501-4_41"},{"key":"ref_18","unstructured":"Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., and Zhang, Z. (2015). MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. arXiv."},{"key":"ref_19","unstructured":"Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/12\/1939\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:36:59Z","timestamp":1760179019000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/12\/1939"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,25]]},"references-count":20,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["sym12121939"],"URL":"https:\/\/doi.org\/10.3390\/sym12121939","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2020,11,25]]}}}