Machine Learning Based Taxonomy and Analysis of English Learners' Translation Errors

IF 0.7 Q3 EDUCATION & EDUCATIONAL RESEARCH
Ying Qin
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引用次数: 2

Abstract

This study extracts the comments from a large scale of Chinese EFL learners' translation corpus to study the taxonomy of translation errors. Two unsupervised machine learning approaches are used to obtain the computational evidences of translation error taxonomy. After manually revision, ten types of English to Chinese (E2C) and eight types Chinese to English (C2E) translation errors are finally confirmed. There probably exists three categories of top-level errors according to the hierarchical clustering results. In addition, three supervised learning methods are applied to automatically recognize the types of errors, among which the highest performance reaches F1 = 0.85 on E2C and F1 = 0.90 on C2E translation. Further comparison to the intuitive or theoretical studies on translation taxonomy shows some phenomenon accompanied by language skill improvement of Chinese learners. Analysis on translation problems based on machine learning provides the objective insight and understanding on the students' translations.
基于机器学习的英语学习者翻译错误分类与分析
本研究从大量中国英语学习者的翻译语料库中抽取评论,研究翻译错误的分类。采用两种无监督机器学习方法获得翻译错误分类的计算证据。经过人工修改,最终确定了10类英译中(E2C)和8类中译英(C2E)的翻译错误。根据分层聚类结果,可能存在三类顶层误差。此外,采用三种监督学习方法自动识别错误类型,其中在E2C和C2E翻译上的最高性能分别达到F1 = 0.85和F1 = 0.90。进一步对比翻译分类法的直观或理论研究,可以发现一些现象伴随着中国学习者语言技能的提高。基于机器学习的翻译问题分析为学生的翻译提供了客观的洞察和理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.00
自引率
14.30%
发文量
49
期刊介绍: The mission of the International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT) is to publish research, theory, and conceptually-based papers that address the use and impact of and innovations in education technologies in advancing foreign/second language learning and teaching. This journal expands on the principles, theories, designs, discussion, and implementations of computer-assisted language learning. In addition to original research papers and submissions on theory and concept development and systematic reports of practice, this journal welcomes theory-based CALL-related book and software/application reviews.
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