Evaluation of Machine Translation Output in Context of Inflectional Languages

被引:0
|
作者
Munkova, Dasa [1 ]
Kapusta, Jozef [2 ]
Munk, Michal [2 ]
Reichel, Jaroslav [2 ]
机构
[1] Constantine Philosopher Univ Nitra, Dept Translat Studies, Nitra, Slovakia
[2] Constantine Philosopher Univ Nitra, Dept Informat, Nitra, Slovakia
来源
2016 IEEE 10TH INTERNATIONAL CONFERENCE ON APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES (AICT) | 2016年
关键词
Manual evaluation; Automatic evaluation; Inflectional language; Natural language processing;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Objective of the paper is to evaluate metrics of automatic evaluation of machine translation output using manual metrics - fluency and adequacy. We tried to answer the question to which extent the manual evaluation correlates with the automatic evaluation of MT output from/to Slovak to/from English. We focused on metrics based on the similarity and statistical principles (WER, PER, CDER and BLEU-n). We found out, that the manual evaluation, namely fluency and adequacy metrics correlates with automatic metrics of MT evaluation for less spoken language and low resource language such as Slovak. The contribution also consists of system proposal for both, manual (based on POS tagging) and automatic (based on reference) evaluation of MT output.
引用
收藏
页码:85 / 89
页数:5
相关论文
共 50 条
  • [31] Neural Machine Translation as a Novel Approach to Machine Translation
    Benkova, Lucia
    Benko, Lubomir
    DIVAI 2020: 13TH INTERNATIONAL SCIENTIFIC CONFERENCE ON DISTANCE LEARNING IN APPLIED INFORMATICS, 2020, : 499 - 508
  • [32] Machine Assisted Translation of Health Materials to Chinese: An Initial Evaluation
    Turner, Anne M.
    Desai, Loma
    Dew, Kristin
    Martin, Nathalie
    Kirchhoff, Katrin
    MEDINFO 2015: EHEALTH-ENABLED HEALTH, 2015, 216 : 979 - 979
  • [33] Improvement of Machine Translation Evaluation by Simple Linguistically Motivated Features
    杨沐昀
    孙叔琦
    朱俊国
    李生
    赵铁军
    朱晓宁
    Journal of Computer Science & Technology, 2011, 26 (01) : 57 - 67
  • [34] Improvement of Machine Translation Evaluation by Simple Linguistically Motivated Features
    Yang, Mu-Yun
    Sun, Shu-Qi
    Zhu, Jun-Guo
    Li, Sheng
    Zhao, Tie-Jun
    Zhu, Xiao-Ning
    JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2011, 26 (01) : 57 - 67
  • [35] Research on Machine Translation Automatic Evaluation Based on Extended Reference
    Li, Na
    Su, Wentao
    Li, Yang
    Yu, Hui
    Xu, Weizhi
    Gao, Baozhong
    2019 IEEE 2ND INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION ENGINEERING TECHNOLOGY (CCET), 2019, : 41 - 45
  • [36] Improving BERTScore for Machine Translation Evaluation Through Contrastive Learning
    Tang, Gongbo
    Yousuf, Oreen
    Jin, Zeying
    IEEE ACCESS, 2024, 12 : 77739 - 77749
  • [37] Evaluation of English-Slovak Neural and Statistical Machine Translation
    Benkova, Lucia
    Munkova, Dasa
    Benko, Lubomir
    Munk, Michal
    APPLIED SCIENCES-BASEL, 2021, 11 (07):
  • [38] Improvement of Machine Translation Evaluation by Simple Linguistically Motivated Features
    Mu-Yun Yang
    Shu-Qi Sun
    Jun-Guo Zhu
    Sheng Li
    Tie-Jun Zhao
    Xiao-Ning Zhu
    Journal of Computer Science and Technology, 2011, 26 : 57 - 67
  • [39] Automatic Machine Translation Evaluation with Part-of-Speech Information
    Han, Aaron L. -F.
    Wong, Derek F.
    Chao, Lidia S.
    He, Liangye
    TEXT, SPEECH, AND DIALOGUE, TSD 2013, 2013, 8082 : 121 - 128
  • [40] Preservation of sentiment in machine translation of low-resource languages: a case study on Slovak movie subtitles
    Reichel, Jaroslav
    Benko, Lubomir
    LANGUAGE RESOURCES AND EVALUATION, 2024, : 779 - 805