Predictive translation memory: a mixed-initiative system for human language translation

Spence Green, Jason Chuang, Jeffrey Heer, Christopher D. Manning
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引用次数: 61

Abstract

The standard approach to computer-aided language translation is post-editing: a machine generates a single translation that a human translator corrects. Recent studies have shown this simple technique to be surprisingly effective, yet it underutilizes the complementary strengths of precision-oriented humans and recall-oriented machines. We present Predictive Translation Memory, an interactive, mixed-initiative system for human language translation. Translators build translations incrementally by considering machine suggestions that update according to the user's current partial translation. In a large-scale study, we find that professional translators are slightly slower in the interactive mode yet produce slightly higher quality translations despite significant prior experience with the baseline post-editing condition. Our analysis identifies significant predictors of time and quality, and also characterizes interactive aid usage. Subjects entered over 99% of characters via interactive aids, a significantly higher fraction than that shown in previous work.
预测翻译记忆:一个用于人类语言翻译的混合主动系统
计算机辅助语言翻译的标准方法是后期编辑:机器生成一个翻译,由人工翻译进行校正。最近的研究表明,这种简单的技术惊人地有效,但它没有充分利用以精确为导向的人类和以记忆为导向的机器的互补优势。我们提出预测翻译记忆,一个交互式的,混合主动系统的人类语言翻译。翻译人员通过考虑根据用户当前部分翻译更新的机器建议来增量地构建翻译。在一项大规模的研究中,我们发现专业翻译人员在交互模式下的翻译速度略慢,但翻译质量略高,尽管他们对基本的后期编辑条件有丰富的经验。我们的分析确定了时间和质量的重要预测因素,并且还确定了交互式援助使用的特征。研究对象通过交互式辅助工具输入了超过99%的字符,这一比例明显高于之前的研究结果。
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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