Deep learning for English grammatical error correction
Rule-based approach and deep learning approach are two most popular approaches while dealing with Grammatical Error Correction (GEC) task. The rule-based approach is strict, fast and precise but unable to deal with complex errors. The deep learning approach is more powerful with the ability to deal...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/149459 |
| _version_ | 1826113472717389824 |
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| author | Luo, Jingying |
| author2 | Hui Siu Cheung |
| author_facet | Hui Siu Cheung Luo, Jingying |
| author_sort | Luo, Jingying |
| collection | NTU |
| description | Rule-based approach and deep learning approach are two most popular approaches while dealing with Grammatical Error Correction (GEC) task. The rule-based approach is strict, fast and precise but unable to deal with complex errors. The deep learning approach is more powerful with the ability to deal with complex or semantic errors, but minor errors are sometimes ignored due to the complexity of neural networks.
This Final Year Project report has investigated the Encoder-Decoder based Sequence-to-Sequence deep learning model on GEC task and incorporated it with the rule-based pre-processing approach. The Deep Dynamic BERT (Bidirectional Encoder Representation from Transformer) -fused model is proposed with GLEU score result of 61.0 on JFLEG system. By incorporating rule-based pre-processing into the model, the system is able to deal with more detailed grammatical errors. The performance was improved especially on the errors at beginner’s level. What is more, a web application prototype with the ability to automatically generate suggestions for grammatical error correction is also built to demonstrate the capability of the model. |
| first_indexed | 2024-10-01T03:23:47Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/149459 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:23:47Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1494592023-07-07T18:15:24Z Deep learning for English grammatical error correction Luo, Jingying Hui Siu Cheung Lin Zhiping School of Electrical and Electronic Engineering EZPLin@ntu.edu.sg, ASSCHUI@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering Rule-based approach and deep learning approach are two most popular approaches while dealing with Grammatical Error Correction (GEC) task. The rule-based approach is strict, fast and precise but unable to deal with complex errors. The deep learning approach is more powerful with the ability to deal with complex or semantic errors, but minor errors are sometimes ignored due to the complexity of neural networks. This Final Year Project report has investigated the Encoder-Decoder based Sequence-to-Sequence deep learning model on GEC task and incorporated it with the rule-based pre-processing approach. The Deep Dynamic BERT (Bidirectional Encoder Representation from Transformer) -fused model is proposed with GLEU score result of 61.0 on JFLEG system. By incorporating rule-based pre-processing into the model, the system is able to deal with more detailed grammatical errors. The performance was improved especially on the errors at beginner’s level. What is more, a web application prototype with the ability to automatically generate suggestions for grammatical error correction is also built to demonstrate the capability of the model. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-05-31T08:32:14Z 2021-05-31T08:32:14Z 2021 Final Year Project (FYP) Luo, J. (2021). Deep learning for English grammatical error correction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149459 https://hdl.handle.net/10356/149459 en B3310-201 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering Luo, Jingying Deep learning for English grammatical error correction |
| title | Deep learning for English grammatical error correction |
| title_full | Deep learning for English grammatical error correction |
| title_fullStr | Deep learning for English grammatical error correction |
| title_full_unstemmed | Deep learning for English grammatical error correction |
| title_short | Deep learning for English grammatical error correction |
| title_sort | deep learning for english grammatical error correction |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/149459 |
| work_keys_str_mv | AT luojingying deeplearningforenglishgrammaticalerrorcorrection |