@inproceedings{cho-etal-2016-adaptation,
title = "Adaptation and Combination of {NMT} Systems: The {KIT} Translation Systems for {IWSLT} 2016",
author = "Cho, Eunah and
Niehues, Jan and
Ha, Thanh-Le and
Sperber, Matthias and
Mediani, Mohammed and
Waibel, Alex",
editor = {Cettolo, Mauro and
Niehues, Jan and
St{\"u}ker, Sebastian and
Bentivogli, Luisa and
Cattoni, Rolando and
Federico, Marcello},
booktitle = "Proceedings of the 13th International Conference on Spoken Language Translation",
month = dec # " 8-9",
year = "2016",
address = "Seattle, Washington D.C",
publisher = "International Workshop on Spoken Language Translation",
url = "https://aclanthology.org/2016.iwslt-1.16",
abstract = "In this paper, we present the KIT systems of the IWSLT 2016 machine translation evaluation. We participated in the machine translation (MT) task as well as the spoken language language translation (SLT) track for English→German and German→English translation. We use attentional neural machine translation (NMT) for all our submissions. We investigated different methods to adapt the system using small in-domain data as well as methods to train the system on these small corpora. In addition, we investigated methods to combine NMT systems that encode the input as well as the output differently. We combine systems using different vocabularies, reverse translation systems, multi-source translation system. In addition, we used pre-translation systems that facilitate phrase-based machine translation systems. Results show that applying domain adaptation and ensemble technique brings a crucial improvement of 3-4 BLEU points over the baseline system. In addition, system combination using n-best lists yields further 1-2 BLEU points.",
}
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%0 Conference Proceedings
%T Adaptation and Combination of NMT Systems: The KIT Translation Systems for IWSLT 2016
%A Cho, Eunah
%A Niehues, Jan
%A Ha, Thanh-Le
%A Sperber, Matthias
%A Mediani, Mohammed
%A Waibel, Alex
%Y Cettolo, Mauro
%Y Niehues, Jan
%Y Stüker, Sebastian
%Y Bentivogli, Luisa
%Y Cattoni, Rolando
%Y Federico, Marcello
%S Proceedings of the 13th International Conference on Spoken Language Translation
%D 2016
%8 dec 8 9
%I International Workshop on Spoken Language Translation
%C Seattle, Washington D.C
%F cho-etal-2016-adaptation
%X In this paper, we present the KIT systems of the IWSLT 2016 machine translation evaluation. We participated in the machine translation (MT) task as well as the spoken language language translation (SLT) track for English→German and German→English translation. We use attentional neural machine translation (NMT) for all our submissions. We investigated different methods to adapt the system using small in-domain data as well as methods to train the system on these small corpora. In addition, we investigated methods to combine NMT systems that encode the input as well as the output differently. We combine systems using different vocabularies, reverse translation systems, multi-source translation system. In addition, we used pre-translation systems that facilitate phrase-based machine translation systems. Results show that applying domain adaptation and ensemble technique brings a crucial improvement of 3-4 BLEU points over the baseline system. In addition, system combination using n-best lists yields further 1-2 BLEU points.
%U https://aclanthology.org/2016.iwslt-1.16
Markdown (Informal)
[Adaptation and Combination of NMT Systems: The KIT Translation Systems for IWSLT 2016](https://aclanthology.org/2016.iwslt-1.16) (Cho et al., IWSLT 2016)
ACL