Character Mapping and Ad-hoc Adaptation: Edinburgh’s IWSLT 2020 Open Domain Translation System

Pinzhen Chen, Nikolay Bogoychev, Ulrich Germann


Abstract
This paper describes the University of Edinburgh’s neural machine translation systems submitted to the IWSLT 2020 open domain JapaneseChinese translation task. On top of commonplace techniques like tokenisation and corpus cleaning, we explore character mapping and unsupervised decoding-time adaptation. Our techniques focus on leveraging the provided data, and we show the positive impact of each technique through the gradual improvement of BLEU.
Anthology ID:
2020.iwslt-1.14
Volume:
Proceedings of the 17th International Conference on Spoken Language Translation
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | IWSLT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
122–129
Language:
URL:
https://www.aclweb.org/anthology/2020.iwslt-1.14
DOI:
10.18653/v1/2020.iwslt-1.14
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.iwslt-1.14.pdf
Video:
 http://slideslive.com/38929590