摘要
该文提出一种面向句子的汉英口语翻译自动评分方法,选取语义关键词、句子大意和口语流利度作为评分的主要参数。为了提高关键词评分的准确度,该文使用同义词辨析方法,识别考生答题关键词中的同义词;在句子层面,使用可伸展递归自编码(unfolding recursive auto-encoder,URAE)神经网络模型分析考生对句子大意的翻译;最后基于语速(tempo/rate)和语音的分布情况对口语流利度进行评分。综合三种参量加权评分,得到最后翻译质量的评分。实验结果表明,采用该文方法与人工评分结果具有较好的一致性,达到了预期设计目标。
This paper proposes a sentence-based automatic scoring method for Chinese-English oral translation. Three main indicators are designed for evaluating keywords, general idea of sentences and fluency. As for keywords, this paper applies the synonym analysis to identify synonyms in candidate keywords. At the sentence level, the translation of sentences is evaluated by Unfolding Recursive Auto-Encoder(URAE). Then, fluency is scored by the speed of the speech. Finally, the weighted sum of the three indicators is generated as the overall translation quality score. The experimental results demonstrated that this automatic scoring method bears good consistency with manual scoring method.
作者
李心广
陈帅
龙晓岚
LIXinguang;CHENShuai;LONG Xiaolan(Laboratory of Language Engineering and Computing,Guangdong University of Foreign Studies,Guangzhou,Guangdong 510006,China;School of Information Science and Technology,Guangdong University of ForeignStudies,Guangzhou,Guangdong 510006,China)
出处
《中文信息学报》
CSCD
北大核心
2021年第7期54-62,共9页
Journal of Chinese Information Processing
基金
国家自然科学基金(61877013)
全国科学技术名词审定委员会2019年度科研项目(WT2019006)
广东省科技创新战略专项资金(Pdjh2021a0170,Pdjh2021b0176)。
关键词
汉英口语翻译自动评分
同义词辨析
URAE神经网络
口语流利度
automatic scoring method of oral Chinese-English translation
synonym analysis
Unfolding Recursive Auto-Encoder(URAE)neural network
fluency
