@inproceedings{lakumarapu-etal-2020-end,
title = "End-to-End Offline Speech Translation System for {IWSLT} 2020 using Modality Agnostic Meta-Learning",
author = "Lakumarapu, Nikhil Kumar and
Lee, Beomseok and
Indurthi, Sathish Reddy and
Han, Hou Jeung and
Zaidi, Mohd Abbas 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-1.7",
doi = "10.18653/v1/2020.iwslt-1.7",
pages = "73--79",
abstract = "In this paper, we describe the system submitted to the IWSLT 2020 Offline Speech Translation Task. We adopt the Transformer architecture coupled with the meta-learning approach to build our end-to-end Speech-to-Text Translation (ST) system. Our meta-learning approach tackles the data scarcity of the ST task by leveraging the data available from Automatic Speech Recognition (ASR) and Machine Translation (MT) tasks. The meta-learning approach combined with synthetic data augmentation techniques improves the model performance significantly and achieves BLEU scores of 24.58, 27.51, and 27.61 on IWSLT test 2015, MuST-C test, and Europarl-ST test sets respectively.",
}
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%0 Conference Proceedings
%T End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning
%A Lakumarapu, Nikhil Kumar
%A Lee, Beomseok
%A Indurthi, Sathish Reddy
%A Han, Hou Jeung
%A Zaidi, Mohd Abbas
%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 lakumarapu-etal-2020-end
%X In this paper, we describe the system submitted to the IWSLT 2020 Offline Speech Translation Task. We adopt the Transformer architecture coupled with the meta-learning approach to build our end-to-end Speech-to-Text Translation (ST) system. Our meta-learning approach tackles the data scarcity of the ST task by leveraging the data available from Automatic Speech Recognition (ASR) and Machine Translation (MT) tasks. The meta-learning approach combined with synthetic data augmentation techniques improves the model performance significantly and achieves BLEU scores of 24.58, 27.51, and 27.61 on IWSLT test 2015, MuST-C test, and Europarl-ST test sets respectively.
%R 10.18653/v1/2020.iwslt-1.7
%U https://aclanthology.org/2020.iwslt-1.7
%U https://doi.org/10.18653/v1/2020.iwslt-1.7
%P 73-79
Markdown (Informal)
[End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning](https://aclanthology.org/2020.iwslt-1.7) (Lakumarapu et al., IWSLT 2020)
ACL