Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/151890
Title: TRANSLATION MODELS FOR GRAMMATICAL ERROR CORRECTION
Authors: SHAMIL CHOLLAMPATT MUHAMMED ASHRAF
Keywords: grammatical error correction, deep learning, convolutional neural networks, machine translation, quality estimation, natural language processing
Issue Date: 30-Sep-2018
Citation: SHAMIL CHOLLAMPATT MUHAMMED ASHRAF (2018-09-30). TRANSLATION MODELS FOR GRAMMATICAL ERROR CORRECTION. ScholarBank@NUS Repository.
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.
URI: http://scholarbank.nus.edu.sg/handle/10635/151890
Appears in Collections:Ph.D Theses (Open)

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