Paper Info
Title
Many-To-English Machine Translation Tools, Data, And Pretrained Models
Abstract
While there are more than 7000 languages in the world, most translation research efforts have targeted a few high resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTDATA, NLCODEC, and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.
Year
DOI
Venue
2021
10.18653/v1/2021.acl-demo.37
ACL-IJCNLP 2021: THE JOINT CONFERENCE OF THE 59TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 11TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING: PROCEEDINGS OF THE SYSTEM DEMONSTRATIONS
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Thamme Gowda101.69
Zhao Zhang217710.37
Chris A. Mattmann320025.39
Jonathan May446932.83