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Title:
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AN E-TUTOR TOOL FOR GRAMMATICAL ERROR
CORRECTION |
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Author(s):
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Xiaodong Sun, Yanqin Yin, Huanhuan Lv, Pikun Wang, Hongwei Ma and Dongqiang Yang |
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ISBN:
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978-989-8704-23-8 |
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Editors:
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Pedro Isaías |
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Year:
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2020 |
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Edition:
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Single |
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Keywords:
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e-Tutor Tool, Data Augmentation, Grammatical Error Correction |
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Type:
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Short |
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First Page:
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161 |
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Last Page:
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165 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Intelligent language learning tools have evolved into an inevitable aid for ESL/EFL (English as second or foreign
language) learners to improve their linguistic skills. By functioning as a machine translation task, automatic grammatical
error correction (GEC) has made significant progress with the help of deep neural networks, but its accuracy and
coverage rates on different error types have not been fully satisfactory in practice. This paper designs an e-Tutor tool for
GEC to help ESL/EFL learners automatically inspect and correct their grammatical errors in writing. One of the core
research tasks in GEC is how to improve its generalizability while dealing with more complex error types. In the paper,
we propose a novel data augmentation method to add artificial noise to native English corpora during training a neural
translation model for GEC. To improve the output quality of GEC, we also design a re-editing module, which mainly
consists of a statistical language model, along with a grammatical error detection classifier, in validating each sentence
generated by GEC. Experiment results on GEC show that our e-Tutor tool can achieve state-of-the-art performance on the
CoNLL-2014 dataset. |
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