Machine Translation with parfda, Moses, kenlm, nplm, and PRO

Ergun Biçici


Abstract
We build parfda Moses statistical machine translation (SMT) models for most language pairs in the news translation task. We experiment with a hybrid approach using neural language models integrated into Moses. We obtain the constrained data statistics on the machine translation task, the coverage of the test sets, and the upper bounds on the translation results. We also contribute a new testsuite for the German-English language pair and a new automated key phrase extraction technique for the evaluation of the testsuite translations.
Anthology ID:
W19-5306
Original:
W19-5306v1
Version 2:
W19-5306v2
Version 3:
W19-5306v3
Volume:
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WMT | WS
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
122–128
Language:
URL:
https://www.aclweb.org/anthology/W19-5306
DOI:
10.18653/v1/W19-5306
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-5306.pdf