@inproceedings{han-etal-2020-end,
title = "End-to-End Simultaneous Translation System for {IWSLT}2020 Using Modality Agnostic Meta-Learning",
author = "Han, Hou Jeung and
Zaidi, Mohd Abbas and
Indurthi, Sathish Reddy and
Lakumarapu, Nikhil Kumar and
Lee, Beomseok and
Kim, Sangha",
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-1.5",
doi = "10.18653/v1/2020.iwslt-1.5",
pages = "62--68",
abstract = "In this paper, we describe end-to-end simultaneous speech-to-text and text-to-text translation systems submitted to IWSLT2020 online translation challenge. The systems are built by adding wait-k and meta-learning approaches to the Transformer architecture. The systems are evaluated on different latency regimes. The simultaneous text-to-text translation achieved a BLEU score of 26.38 compared to the competition baseline score of 14.17 on the low latency regime (Average latency {\mbox{$\leq$}} 3). The simultaneous speech-to-text system improves the BLEU score by 7.7 points over the competition baseline for the low latency regime (Average Latency {\mbox{$\leq$}} 1000).",
}
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<abstract>In this paper, we describe end-to-end simultaneous speech-to-text and text-to-text translation systems submitted to IWSLT2020 online translation challenge. The systems are built by adding wait-k and meta-learning approaches to the Transformer architecture. The systems are evaluated on different latency regimes. The simultaneous text-to-text translation achieved a BLEU score of 26.38 compared to the competition baseline score of 14.17 on the low latency regime (Average latency łeq 3). The simultaneous speech-to-text system improves the BLEU score by 7.7 points over the competition baseline for the low latency regime (Average Latency łeq 1000).</abstract>
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%0 Conference Proceedings
%T End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning
%A Han, Hou Jeung
%A Zaidi, Mohd Abbas
%A Indurthi, Sathish Reddy
%A Lakumarapu, Nikhil Kumar
%A Lee, Beomseok
%A Kim, Sangha
%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 han-etal-2020-end
%X In this paper, we describe end-to-end simultaneous speech-to-text and text-to-text translation systems submitted to IWSLT2020 online translation challenge. The systems are built by adding wait-k and meta-learning approaches to the Transformer architecture. The systems are evaluated on different latency regimes. The simultaneous text-to-text translation achieved a BLEU score of 26.38 compared to the competition baseline score of 14.17 on the low latency regime (Average latency łeq 3). The simultaneous speech-to-text system improves the BLEU score by 7.7 points over the competition baseline for the low latency regime (Average Latency łeq 1000).
%R 10.18653/v1/2020.iwslt-1.5
%U https://aclanthology.org/2020.iwslt-1.5
%U https://doi.org/10.18653/v1/2020.iwslt-1.5
%P 62-68
Markdown (Informal)
[End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning](https://aclanthology.org/2020.iwslt-1.5) (Han et al., IWSLT 2020)
ACL