@inproceedings{matusov-etal-2018-neural,
title = "Neural Speech Translation at {A}pp{T}ek",
author = "Matusov, Evgeny and
Wilken, Patrick and
Bahar, Parnia and
Schamper, Julian and
Golik, Pavel and
Zeyer, Albert and
Silvestre-Cerda, Joan Albert and
Mart{\'\i}nez-Villaronga, Adri{\`a} and
Pesch, Hendrik and
Peter, Jan-Thorsten",
booktitle = "Proceedings of the 15th International Conference on Spoken Language Translation",
month = oct # " 29-30",
year = "2018",
address = "Brussels",
publisher = "International Conference on Spoken Language Translation",
url = "https://aclanthology.org/2018.iwslt-1.15",
pages = "104--111",
abstract = "This work describes AppTek{'}s speech translation pipeline that includes strong state-of-the-art automatic speech recognition (ASR) and neural machine translation (NMT) components. We show how these components can be tightly coupled by encoding ASR confusion networks, as well as ASR-like noise adaptation, vocabulary normalization, and implicit punctuation prediction during translation. In another experimental setup, we propose a direct speech translation approach that can be scaled to translation tasks with large amounts of text-only parallel training data but a limited number of hours of recorded and human-translated speech.",
}
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<abstract>This work describes AppTek’s speech translation pipeline that includes strong state-of-the-art automatic speech recognition (ASR) and neural machine translation (NMT) components. We show how these components can be tightly coupled by encoding ASR confusion networks, as well as ASR-like noise adaptation, vocabulary normalization, and implicit punctuation prediction during translation. In another experimental setup, we propose a direct speech translation approach that can be scaled to translation tasks with large amounts of text-only parallel training data but a limited number of hours of recorded and human-translated speech.</abstract>
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%0 Conference Proceedings
%T Neural Speech Translation at AppTek
%A Matusov, Evgeny
%A Wilken, Patrick
%A Bahar, Parnia
%A Schamper, Julian
%A Golik, Pavel
%A Zeyer, Albert
%A Silvestre-Cerda, Joan Albert
%A Martínez-Villaronga, Adrià
%A Pesch, Hendrik
%A Peter, Jan-Thorsten
%S Proceedings of the 15th International Conference on Spoken Language Translation
%D 2018
%8 oct" 29 30"
%I International Conference on Spoken Language Translation
%C Brussels
%F matusov-etal-2018-neural
%X This work describes AppTek’s speech translation pipeline that includes strong state-of-the-art automatic speech recognition (ASR) and neural machine translation (NMT) components. We show how these components can be tightly coupled by encoding ASR confusion networks, as well as ASR-like noise adaptation, vocabulary normalization, and implicit punctuation prediction during translation. In another experimental setup, we propose a direct speech translation approach that can be scaled to translation tasks with large amounts of text-only parallel training data but a limited number of hours of recorded and human-translated speech.
%U https://aclanthology.org/2018.iwslt-1.15
%P 104-111
Markdown (Informal)
[Neural Speech Translation at AppTek](https://aclanthology.org/2018.iwslt-1.15) (Matusov et al., IWSLT 2018)
ACL
- Evgeny Matusov, Patrick Wilken, Parnia Bahar, Julian Schamper, Pavel Golik, Albert Zeyer, Joan Albert Silvestre-Cerda, Adrià Martínez-Villaronga, Hendrik Pesch, and Jan-Thorsten Peter. 2018. Neural Speech Translation at AppTek. In Proceedings of the 15th International Conference on Spoken Language Translation, pages 104–111, Brussels. International Conference on Spoken Language Translation.