Automatic Evaluation of Translation Quality Using Ezpanded N-gram Co-occurrence

摘要：

BLEU and NIST as official machine translation evaluation metrics are widely used to assess system translation quality. These n-gram co-occurrence algorithms are applied to evaluate language learners’ translations in this paper. Subtle differences of evaluation on machine translation and learners’ translation are discussed. Dependent on n-gram matching between target translation and references, BLEU and NIST evaluate translation quality completely disregarding the source language. Based on sense overlapping in original language, we make pseudo translations for BLEU and NIST by substituting words or phrases in target translation for synonymous words and phrases in references. Pseudo translations expand n-gram co-occurrence between target translation and references. Evaluation experiments on learners’ translation and machine translation corpus with expanded n-gram co-occurrence outperform pure BLEU and NIST evaluation in higher correlation with human assessments.

关键词： Machine translation evaluation n-gram co-occurrence learners translation evaluation

作者: Ying QIN Qiufang WEN Jinquan WANG

作者单位: National Research Centre for Foreign Language Education, Beijing Foreign Studies University, Beijing School of Foreign Languages, Yangzhou University, Yangzhou

会议类型: 国际会议

会议名称: International Conference on Natural Language Processing and Knowledge Engineering(IEEE自然语言处理与知识工程国际会议 IEEE NLP-KE 2009)

会议地点: 大连

会议语种:英文

页码: 1-5

在线出版日期: 2009-09-24（万方平台首次上网日期，不代表论文的发表时间）