Chapter 4. An eye-tracking study of productivity and effort in Chinese-to-English translation and post-editing

Author:

Sun Juan1,Lu Zhi2,Lacruz Isabel3,Ma Lijun4,Fan Lin5,Huang Xiuhua6,Zhou Bo7

Affiliation:

1. Shandong University of Finance and Economics

2. Guangdong University of Foreign Studies

3. Kent State University

4. Guangzhou University of Chinese Medicine

5. Beijing Foreign Studies University

6. Guangdong University of Foreign Studies/Guangdong University of Finance

7. Guangzhou Municipal Foreign Affairs Office

Abstract

For several language pairs, an emerging consensus finds that post-editing of machine translations is faster and less cognitively effortful than from-scratch human translation, resulting in increased translator productivity and decreased translator fatigue. These benefits have yet to be robustly established in some language pairs that are linguistically and culturally remote with very different writing systems. We carry out a systematic Chinese-to-English study using keystroke logger timing measures and eye-tracking measures of cognitive effort, taking into account translator education levels, different source text domains, and quality of the translation product. We observe significant post-editing productivity gains for more highly educated participants and for more straightforward and less technical texts. Measures of cognitive effort show significantly reduced cognitive effort in post-editing.

Publisher

John Benjamins Publishing Company

Reference54 articles.

1. Correcting automatic translations through collaborations between MT and monolingual target-language users

2. Production Time across Languages and Tasks: A Large-scale Analysis Using the CRITT Translation Process Database;Balling,2014

3. Confidence estimation for machine translation

4. Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation;Callison-Burch,2010

5. Findings of the 2012 Joint Workshop on Statistical Machine Translation;Callison-Burch,2012

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3