SGER: Learning Syntax-based Evaluation Metrics for Machine Translation

SGER：学习基于语法的机器翻译评估指标

• 批准号: 0612791
• 负责人: Rebecca Hwa
• 金额: --
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0612791&HistoricalAwards=false
• 关键词: SGER; Learning; Syntax; based; Evaluation

Recent years have witnessed significant improvements in automaticmachine translation systems. An enabling factor for this shortturnaround time is the adoption of automatic evaluation metrics, whichserve to guide developers to improve translation systems. However,current metrics are a coarse approximation of human judgments forsystem outputs. This SGER project is exploring ways of improvingcurrent metrics. It does so on three fronts. First, judgments ofsyntactic similarities between system outputs and human references arebeing incorporated into the metric in addition to phrasalsimilarities. Second, machine learning techniques are being appliedto find characteristics that are correlated to human judgments ofquality. Third, the project is determining more fine-grained analysesof what is wrong as well as what is right with system outputs based onsyntactic as well as lexical characteristics of the system outputs.In developing a learnable, syntax-aware metric, discriminative methodswill be generalized for complex, structured problems. A moreinformative automatic evaluation metric will lead to more rapidimprovements in machine translation technology, enabling access toarchives from centuries past and documents from around the world. Allrelevant software and non-proprietary data will be distributed widely.Written reports of experimental results and findings will bedisseminated through academic publications.

近年来，自动机器翻译系统有了显着改善。 在这个短期时间内，一个有能力的因素是采用自动评估指标，该指标旨在指导开发人员改善翻译系统。 但是，当前的指标是人类判断的伪造系统输出的粗略近似。 这个SGER项目正在探索改善电流指标的方法。 它在三个方面这样做。 首先，除了表情相似性外，系统产出与人类参考之间的句法相似性纳入了度量标准。 其次，正在应用机器学习技术来查找与人类质量判断相关的特征。 第三，该项目是确定错误的分析，以及基于系统输出的系统输出以及系统输出的正确性。 更具信息的自动评估度量标准将导致机器翻译技术的更快，从而使过去几个世纪的访问到达统治者和来自世界各地的文档。 Allrelevant软件和非专有数据将被广泛分发。实验结果和发现的报道将通过学术出版物进行覆盖。