@inproceedings{fukuda-etal-2020-naists,
title = "{NAIST}{'}s Machine Translation Systems for {IWSLT} 2020 Conversational Speech Translation Task",
author = "Fukuda, Ryo and
Sudoh, Katsuhito and
Nakamura, Satoshi",
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-1.21",
doi = "10.18653/v1/2020.iwslt-1.21",
pages = "172--177",
abstract = "This paper describes NAIST{'}s NMT system submitted to the IWSLT 2020 conversational speech translation task. We focus on the translation disfluent speech transcripts that include ASR errors and non-grammatical utterances. We tried a domain adaptation method by transferring the styles of out-of-domain data (United Nations Parallel Corpus) to be like in-domain data (Fisher transcripts). Our system results showed that the NMT model with domain adaptation outperformed a baseline. In addition, slight improvement by the style transfer was observed.",
}
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%0 Conference Proceedings
%T NAIST’s Machine Translation Systems for IWSLT 2020 Conversational Speech Translation Task
%A Fukuda, Ryo
%A Sudoh, Katsuhito
%A Nakamura, Satoshi
%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 fukuda-etal-2020-naists
%X This paper describes NAIST’s NMT system submitted to the IWSLT 2020 conversational speech translation task. We focus on the translation disfluent speech transcripts that include ASR errors and non-grammatical utterances. We tried a domain adaptation method by transferring the styles of out-of-domain data (United Nations Parallel Corpus) to be like in-domain data (Fisher transcripts). Our system results showed that the NMT model with domain adaptation outperformed a baseline. In addition, slight improvement by the style transfer was observed.
%R 10.18653/v1/2020.iwslt-1.21
%U https://aclanthology.org/2020.iwslt-1.21
%U https://doi.org/10.18653/v1/2020.iwslt-1.21
%P 172-177
Markdown (Informal)
[NAIST’s Machine Translation Systems for IWSLT 2020 Conversational Speech Translation Task](https://aclanthology.org/2020.iwslt-1.21) (Fukuda et al., IWSLT 2020)
ACL