A preliminary evaluation of metadata records machine translation
A preliminary evaluation of metadata records machine translation
复制标题
元数据记录机器翻译的初步评价
DOI：
10.1108/02640471211221377
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Ryan Knudson
中科院分区：
文献类型：
--
作者：
Jiangping Chen;Ren Ding;Shan Jiang;Ryan Knudson
目的-本研究的目的是评估免费提供的机器翻译(MT)服务在翻译元数据记录方面的性能。设计/方法/方法-使用Google、Bing和Systran机器翻译系统将随机选择的元数据记录从英文翻译成中文。然后使用五分制对这些翻译进行流利性和充分性的评估。结果--关于流畅性和充分性，谷歌和必应翻译的超过70%的测试数据得到了等于或大于3的分数，分别代表“非母语汉语”和“覆盖面很广”。Systran在这两项指标中得分最低。然而，这些差异在统计学上并不显著。皮尔逊相关分析表明，流利性和充分性之间存在很强的相关性(r=0.86)。遗漏计数和错误计数与流利性和广告性有很强的相关性。
Purpose – The purpose of this study is to evaluate freely available machine translation (MT) services' performance in translating metadata records.Design/methodology/approach – Randomly selected metadata records were translated from English into Chinese using Google, Bing, and SYSTRAN MT systems. These translations were then evaluated using a five point scale for both fluency and adequacy. Missing count (words not translated) and incorrect count (words incorrectly translated) were also recorded.Findings – Concerning both fluency and adequacy, Google and Bing's translations of more than 70 percent of test data received scores equal to or greater than three, representative of “non‐native Chinese” and “much coverage,” respectively. SYSTRAN scored lowest in both measures. However, these differences were not statistically significant. A Pearson correlation analysis demonstrated a strong relationship (r=0.86) between fluency and adequacy. Missing count and incorrect count strongly correlated with fluency and ad...