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        <title>The UMD Machine Translation Systems at IWSLT 2016: English-to-French Translation of Speech Transcripts</title>
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    <name type="personal">
        <namePart type="given">Xing</namePart>
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            <title>Proceedings of the 13th International Conference on Spoken Language Translation</title>
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            <namePart type="given">Mauro</namePart>
            <namePart type="family">Cettolo</namePart>
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            <namePart type="given">Jan</namePart>
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            <namePart type="given">Luisa</namePart>
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        <name type="personal">
            <namePart type="given">Marcello</namePart>
            <namePart type="family">Federico</namePart>
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    <abstract>We describe the University of Maryland machine translation system submitted to the IWSLT 2016 Microsoft Speech Language Translation (MSLT) English-French task. Our main finding is that translating conversation transcripts turned out to not be as challenging as we expected: while translation quality is of course not perfect, a straightforward phrase-based system trained on movie subtitles yields high BLEU scores (high 40s on the development set) and manual analysis of 100 examples showed that 61 of them were correctly translated, and errors were mostly local disfluencies in the remaining examples.</abstract>
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%0 Conference Proceedings
%T The UMD Machine Translation Systems at IWSLT 2016: English-to-French Translation of Speech Transcripts
%A Niu, Xing
%A Carpuat, Marine
%Y Cettolo, Mauro
%Y Niehues, Jan
%Y Stüker, Sebastian
%Y Bentivogli, Luisa
%Y Cattoni, Rolando
%Y Federico, Marcello
%S Proceedings of the 13th International Conference on Spoken Language Translation
%D 2016
%8 dec 8 9
%I International Workshop on Spoken Language Translation
%C Seattle, Washington D.C
%F niu-carpuat-2016-umd
%X We describe the University of Maryland machine translation system submitted to the IWSLT 2016 Microsoft Speech Language Translation (MSLT) English-French task. Our main finding is that translating conversation transcripts turned out to not be as challenging as we expected: while translation quality is of course not perfect, a straightforward phrase-based system trained on movie subtitles yields high BLEU scores (high 40s on the development set) and manual analysis of 100 examples showed that 61 of them were correctly translated, and errors were mostly local disfluencies in the remaining examples.
%U https://aclanthology.org/2016.iwslt-1.25

Markdown (Informal)

[The UMD Machine Translation Systems at IWSLT 2016: English-to-French Translation of Speech Transcripts](https://aclanthology.org/2016.iwslt-1.25) (Niu & Carpuat, IWSLT 2016)

• The UMD Machine Translation Systems at IWSLT 2016: English-to-French Translation of Speech Transcripts (Niu & Carpuat, IWSLT 2016)

ACL

• Xing Niu and Marine Carpuat. 2016. The UMD Machine Translation Systems at IWSLT 2016: English-to-French Translation of Speech Transcripts. In Proceedings of the 13th International Conference on Spoken Language Translation, Seattle, Washington D.C. International Workshop on Spoken Language Translation.