Automatic assessment on English-to-Chinese translation for personalized learning

This project aims to investigate automatic assessment techniques for students’ English to Chinese translation writings (such as ESL English). In English to Chinese translations, students are required to submit the translated Chinese answers for marking. This project only aims to grade (assess) the s...

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Bibliographic Details
Main Author: Chen, Zhe
Other Authors: Hui Siu Cheung
Format: Final Year Project (FYP)
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10356/66738
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author Chen, Zhe
author2 Hui Siu Cheung
author_facet Hui Siu Cheung
Chen, Zhe
author_sort Chen, Zhe
collection NTU
description This project aims to investigate automatic assessment techniques for students’ English to Chinese translation writings (such as ESL English). In English to Chinese translations, students are required to submit the translated Chinese answers for marking. This project only aims to grade (assess) the students’ translations in Chinese (not automatic translation techniques). Manual marking of students translation writings is difficult as it is time consuming and difficult to grade. To assess the students' translation writings automatically, we need to look into different aspects such as rhetoric (readability, vocabulary, grammar, etc.), organization (conjunctions, etc.) and content (vocabulary related to the topic) of the translations. In this project, we will look into the different techniques in grading students’ translations automatically, especially to provide feedback on the translations. Currently automatic assessment techniques are mainly categorized as supervised such as Support Vector Machine and unsupervised such as Latent Semantic Analysis.
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spelling ntu-10356/667382023-03-03T20:44:41Z Automatic assessment on English-to-Chinese translation for personalized learning Chen, Zhe Hui Siu Cheung School of Computer Engineering DRNTU::Engineering This project aims to investigate automatic assessment techniques for students’ English to Chinese translation writings (such as ESL English). In English to Chinese translations, students are required to submit the translated Chinese answers for marking. This project only aims to grade (assess) the students’ translations in Chinese (not automatic translation techniques). Manual marking of students translation writings is difficult as it is time consuming and difficult to grade. To assess the students' translation writings automatically, we need to look into different aspects such as rhetoric (readability, vocabulary, grammar, etc.), organization (conjunctions, etc.) and content (vocabulary related to the topic) of the translations. In this project, we will look into the different techniques in grading students’ translations automatically, especially to provide feedback on the translations. Currently automatic assessment techniques are mainly categorized as supervised such as Support Vector Machine and unsupervised such as Latent Semantic Analysis. Bachelor of Engineering (Computer Science) 2016-04-25T02:14:14Z 2016-04-25T02:14:14Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/66738 en Nanyang Technological University 102 p. application/pdf
spellingShingle DRNTU::Engineering
Chen, Zhe
Automatic assessment on English-to-Chinese translation for personalized learning
title Automatic assessment on English-to-Chinese translation for personalized learning
title_full Automatic assessment on English-to-Chinese translation for personalized learning
title_fullStr Automatic assessment on English-to-Chinese translation for personalized learning
title_full_unstemmed Automatic assessment on English-to-Chinese translation for personalized learning
title_short Automatic assessment on English-to-Chinese translation for personalized learning
title_sort automatic assessment on english to chinese translation for personalized learning
topic DRNTU::Engineering
url http://hdl.handle.net/10356/66738
work_keys_str_mv AT chenzhe automaticassessmentonenglishtochinesetranslationforpersonalizedlearning