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https://scholarbank.nus.edu.sg/handle/10635/151890
DC Field | Value | |
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dc.title | TRANSLATION MODELS FOR GRAMMATICAL ERROR CORRECTION | |
dc.contributor.author | SHAMIL CHOLLAMPATT MUHAMMED ASHRAF | |
dc.date.accessioned | 2019-02-28T18:12:53Z | |
dc.date.available | 2019-02-28T18:12:53Z | |
dc.date.issued | 2018-09-30 | |
dc.identifier.citation | SHAMIL CHOLLAMPATT MUHAMMED ASHRAF (2018-09-30). TRANSLATION MODELS FOR GRAMMATICAL ERROR CORRECTION. ScholarBank@NUS Repository. | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/151890 | |
dc.description.abstract | Grammatical error correction (GEC) automatically corrects various kinds of errors in writing, including spelling, punctuation, grammatical, and word choice errors. This thesis explores a machine translation (MT) approach which treats GEC as a translation task from “bad English” to “good English”. Apart from investigating several features for the statistical MT (SMT) approach, an SMT component for spelling error correction (Spell-MT) employing a character-level model is proposed. A domain-adapted neural network joint model is incorporated in the SMT framework to improve its generalization. Further, a multilayer convolutional encoder-decoder network is proposed for GEC, employing several techniques such as transfer learning and feature-based re-ranking and incorporating Spell-MT. The proposed approach achieves the highest published F0.5 score on the benchmark CoNLL-2014 test data, based on training on non-proprietary training data. Neural quality estimation (QE) models are proposed to assess the quality of real-time GEC outputs and to improve downstream GEC. | |
dc.language.iso | en | |
dc.subject | grammatical error correction, deep learning, convolutional neural networks, machine translation, quality estimation, natural language processing | |
dc.type | Thesis | |
dc.contributor.department | DEAN'S OFFICE (NGS FOR INTGR SCI & ENGG) | |
dc.contributor.supervisor | NG HWEE TOU | |
dc.description.degree | Ph.D | |
dc.description.degreeconferred | DOCTOR OF PHILOSOPHY | |
Appears in Collections: | Ph.D Theses (Open) |
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ChollampattS.pdf | 1.08 MB | Adobe PDF | OPEN | None | View/Download |
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