@inproceedings{farajian-etal-2016-fbks,
title = "{FBK}{'}s Neural Machine Translation Systems for {IWSLT} 2016",
author = "Farajian, M. Amin and
Chatterjee, Rajen and
Conforti, Costanza and
Jalalvand, Shahab and
Balaraman, Vevake and
Di Gangi, Mattia A. and
Ataman, Duygu and
Turchi, Marco and
Negri, Matteo and
Federico, Marcello",
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-1.15",
abstract = "In this paper, we describe FBK{'}s neural machine translation (NMT) systems submitted at the International Workshop on Spoken Language Translation (IWSLT) 2016. The systems are based on the state-of-the-art NMT architecture that is equipped with a bi-directional encoder and an attention mechanism in the decoder. They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words. We compare performances of word and subword NMT systems along with different optimizers. Further, we explore different ensemble techniques to leverage multiple models within the same and across different networks. Several reranking methods are also explored. Our submissions cover all directions of the MSLT task, as well as en-{de, fr} and {de, fr}-en directions of TED. Compared to previously published best results on the TED 2014 test set, our models achieve comparable results on en-de and surpass them on en-fr (+2 BLEU) and fr-en (+7.7 BLEU) language pairs.",
}
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<abstract>In this paper, we describe FBK’s neural machine translation (NMT) systems submitted at the International Workshop on Spoken Language Translation (IWSLT) 2016. The systems are based on the state-of-the-art NMT architecture that is equipped with a bi-directional encoder and an attention mechanism in the decoder. They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words. We compare performances of word and subword NMT systems along with different optimizers. Further, we explore different ensemble techniques to leverage multiple models within the same and across different networks. Several reranking methods are also explored. Our submissions cover all directions of the MSLT task, as well as en-de, fr and de, fr-en directions of TED. Compared to previously published best results on the TED 2014 test set, our models achieve comparable results on en-de and surpass them on en-fr (+2 BLEU) and fr-en (+7.7 BLEU) language pairs.</abstract>
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%0 Conference Proceedings
%T FBK’s Neural Machine Translation Systems for IWSLT 2016
%A Farajian, M. Amin
%A Chatterjee, Rajen
%A Conforti, Costanza
%A Jalalvand, Shahab
%A Balaraman, Vevake
%A Di Gangi, Mattia A.
%A Ataman, Duygu
%A Turchi, Marco
%A Negri, Matteo
%A Federico, Marcello
%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 farajian-etal-2016-fbks
%X In this paper, we describe FBK’s neural machine translation (NMT) systems submitted at the International Workshop on Spoken Language Translation (IWSLT) 2016. The systems are based on the state-of-the-art NMT architecture that is equipped with a bi-directional encoder and an attention mechanism in the decoder. They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words. We compare performances of word and subword NMT systems along with different optimizers. Further, we explore different ensemble techniques to leverage multiple models within the same and across different networks. Several reranking methods are also explored. Our submissions cover all directions of the MSLT task, as well as en-de, fr and de, fr-en directions of TED. Compared to previously published best results on the TED 2014 test set, our models achieve comparable results on en-de and surpass them on en-fr (+2 BLEU) and fr-en (+7.7 BLEU) language pairs.
%U https://aclanthology.org/2016.iwslt-1.15
Markdown (Informal)
[FBK’s Neural Machine Translation Systems for IWSLT 2016](https://aclanthology.org/2016.iwslt-1.15) (Farajian et al., IWSLT 2016)
ACL
- M. Amin Farajian, Rajen Chatterjee, Costanza Conforti, Shahab Jalalvand, Vevake Balaraman, Mattia A. Di Gangi, Duygu Ataman, Marco Turchi, Matteo Negri, and Marcello Federico. 2016. FBK’s Neural Machine Translation Systems for IWSLT 2016. In Proceedings of the 13th International Conference on Spoken Language Translation, Seattle, Washington D.C. International Workshop on Spoken Language Translation.