Sustaining Disruption? The Transition from Statistical to Neural Machine Translation

被引：11
作者：
Kenny, Dorothy [1 ]
机构：
[1] Dublin City Univ, Dublin, Ireland
来源：
TRADUMATICA-TRADUCCIO I TECNOLOGIES DE LA INFORMACIO I LA COMUNICACIO | 2018年 / 16期
关键词：
disruptive innovation; machine translation; statistical MT; neural MT; quality metrics; mobility;
D O I：
10.5565/rev/tradumatica.221
中图分类号：
H0 [语言学];
学科分类号：
030303 ; 0501 ; 050102 ;
摘要：
If statistical machine translation (SMT) was a disruptive technology, then neural machine translation (NMT) is probably a sustaining technology, continuing on a trajectory already established by SMT, and initially evaluated in much the same way as its predecessor. Seeing NMT in this light may be a useful corrective to the hype that has surrounded its introduction.
引用
收藏
页码：59 / 70
页数：12
相关论文
共 38 条
[1]  
[Anonymous], 2015, NON TRADITIONAL REF
[2]  
[Anonymous], 2015, THE ECONOMIST
[3]  
[Anonymous], 2011, ECONOMIST
[4]  
Bentivogli L., 2016, EMNLP 2016
[5]  
Brown P, 1988, P 2 INT C THEOR METH
[6]  
Brynjolfsson E, 2014, 2 MACHINE AGE WORK P
[7]  
Castilho Sheila, 2017, Prague Bulletin of Mathematical Linguistics, P109, DOI 10.1515/pralin-2017-0013
[8]  
Castilho S, 2018, MACH TRANS TECH APPL, V1, P9, DOI 10.1007/978-3-319-91241-7_2
[9]  
Christensen C.M., 1997, INNOVATORS DILEMMA N
[10]  
Christensen C.M., 2003, INNOVATORS SOLUTION
1234