@inproceedings{ore-etal-2020-afrl,
title = "The {AFRL} {IWSLT} 2020 Systems: Work-From-Home Edition",
author = "Ore, Brian and
Hansen, Eric and
Anderson, Tim and
Gwinnup, Jeremy",
editor = {Federico, Marcello and
Waibel, Alex and
Knight, Kevin and
Nakamura, Satoshi and
Ney, Hermann and
Niehues, Jan and
St{\"u}ker, Sebastian and
Wu, Dekai and
Mariani, Joseph and
Yvon, Francois},
booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.iwslt-1.11/",
doi = "10.18653/v1/2020.iwslt-1.11",
pages = "103--108",
abstract = "This report summarizes the Air Force Research Laboratory (AFRL) submission to the offline spoken language translation (SLT) task as part of the IWSLT 2020 evaluation campaign. As in previous years, we chose to adopt the cascade approach of using separate systems to perform speech activity detection, automatic speech recognition, sentence segmentation, and machine translation. All systems were neural based, including a fully-connected neural network for speech activity detection, a Kaldi factorized time delay neural network with recurrent neural network (RNN) language model rescoring for speech recognition, a bidirectional RNN with attention mechanism for sentence segmentation, and transformer networks trained with OpenNMT and Marian for machine translation. Our primary submission yielded BLEU scores of 21.28 on tst2019 and 23.33 on tst2020."
}
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<abstract>This report summarizes the Air Force Research Laboratory (AFRL) submission to the offline spoken language translation (SLT) task as part of the IWSLT 2020 evaluation campaign. As in previous years, we chose to adopt the cascade approach of using separate systems to perform speech activity detection, automatic speech recognition, sentence segmentation, and machine translation. All systems were neural based, including a fully-connected neural network for speech activity detection, a Kaldi factorized time delay neural network with recurrent neural network (RNN) language model rescoring for speech recognition, a bidirectional RNN with attention mechanism for sentence segmentation, and transformer networks trained with OpenNMT and Marian for machine translation. Our primary submission yielded BLEU scores of 21.28 on tst2019 and 23.33 on tst2020.</abstract>
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%0 Conference Proceedings
%T The AFRL IWSLT 2020 Systems: Work-From-Home Edition
%A Ore, Brian
%A Hansen, Eric
%A Anderson, Tim
%A Gwinnup, Jeremy
%Y Federico, Marcello
%Y Waibel, Alex
%Y Knight, Kevin
%Y Nakamura, Satoshi
%Y Ney, Hermann
%Y Niehues, Jan
%Y Stüker, Sebastian
%Y Wu, Dekai
%Y Mariani, Joseph
%Y Yvon, Francois
%S Proceedings of the 17th International Conference on Spoken Language Translation
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F ore-etal-2020-afrl
%X This report summarizes the Air Force Research Laboratory (AFRL) submission to the offline spoken language translation (SLT) task as part of the IWSLT 2020 evaluation campaign. As in previous years, we chose to adopt the cascade approach of using separate systems to perform speech activity detection, automatic speech recognition, sentence segmentation, and machine translation. All systems were neural based, including a fully-connected neural network for speech activity detection, a Kaldi factorized time delay neural network with recurrent neural network (RNN) language model rescoring for speech recognition, a bidirectional RNN with attention mechanism for sentence segmentation, and transformer networks trained with OpenNMT and Marian for machine translation. Our primary submission yielded BLEU scores of 21.28 on tst2019 and 23.33 on tst2020.
%R 10.18653/v1/2020.iwslt-1.11
%U https://aclanthology.org/2020.iwslt-1.11/
%U https://doi.org/10.18653/v1/2020.iwslt-1.11
%P 103-108
Markdown (Informal)
[The AFRL IWSLT 2020 Systems: Work-From-Home Edition](https://aclanthology.org/2020.iwslt-1.11/) (Ore et al., IWSLT 2020)
ACL
- Brian Ore, Eric Hansen, Tim Anderson, and Jeremy Gwinnup. 2020. The AFRL IWSLT 2020 Systems: Work-From-Home Edition. In Proceedings of the 17th International Conference on Spoken Language Translation, pages 103–108, Online. Association for Computational Linguistics.