@inproceedings{schneider-waibel-2020-towards,
title = "Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation",
author = "Schneider, Felix and
Waibel, Alexander",
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-1.28",
doi = "10.18653/v1/2020.iwslt-1.28",
pages = "228--236",
abstract = "Simultaneous machine translation systems rely on a policy to schedule read and write operations in order to begin translating a source sentence before it is complete. In this paper, we demonstrate the use of Adaptive Computation Time (ACT) as an adaptive, learned policy for simultaneous machine translation using the transformer model and as a more numerically stable alternative to Monotonic Infinite Lookback Attention (MILk). We achieve state-of-the-art results in terms of latency-quality tradeoffs. We also propose a method to use our model on unsegmented input, i.e. without sentence boundaries, simulating the condition of translating output from automatic speech recognition. We present first benchmark results on this task.",
}
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%0 Conference Proceedings
%T Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation
%A Schneider, Felix
%A Waibel, Alexander
%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 schneider-waibel-2020-towards
%X Simultaneous machine translation systems rely on a policy to schedule read and write operations in order to begin translating a source sentence before it is complete. In this paper, we demonstrate the use of Adaptive Computation Time (ACT) as an adaptive, learned policy for simultaneous machine translation using the transformer model and as a more numerically stable alternative to Monotonic Infinite Lookback Attention (MILk). We achieve state-of-the-art results in terms of latency-quality tradeoffs. We also propose a method to use our model on unsegmented input, i.e. without sentence boundaries, simulating the condition of translating output from automatic speech recognition. We present first benchmark results on this task.
%R 10.18653/v1/2020.iwslt-1.28
%U https://aclanthology.org/2020.iwslt-1.28
%U https://doi.org/10.18653/v1/2020.iwslt-1.28
%P 228-236
Markdown (Informal)
[Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation](https://aclanthology.org/2020.iwslt-1.28) (Schneider & Waibel, IWSLT 2020)
ACL