The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2018

Ngoc-Quan Pham, Jan Niehues, Alexander Waibel


Abstract
We present our experiments in the scope of the news translation task in WMT 2018, in directions: English→German. The core of our systems is the encoder-decoder based neural machine translation models using the transformer architecture. We enhanced the model with a deeper architecture. By using techniques to limit the memory consumption, we were able to train models that are 4 times larger on one GPU and improve the performance by 1.2 BLEU points. Furthermore, we performed sentence selection for the newly available ParaCrawl corpus. Thereby, we could improve the effectiveness of the corpus by 0.5 BLEU points.
Anthology ID:
W18-6422
Volume:
Proceedings of the Third Conference on Machine Translation: Shared Task Papers
Month:
October
Year:
2018
Address:
Belgium, Brussels
Venues:
EMNLP | WMT | WS
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
467–472
Language:
URL:
https://www.aclweb.org/anthology/W18-6422
DOI:
10.18653/v1/W18-6422
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-6422.pdf