November 2, 2019 Conference paper Open Access

Di Gangi, Matti; Enyedi, Robert; Brusadin, Alessandra; Federico, Marcello

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        <foaf:name>Di Gangi, Matti</foaf:name>
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        <foaf:name>Brusadin, Alessandra</foaf:name>
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        <foaf:name>Federico, Marcello</foaf:name>
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            <foaf:name>Amazon AI, East Palo Alto, USA</foaf:name>
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    <dct:title>Robust Neural Machine Translation for Clean and Noisy Speech Transcripts</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-11-02</dct:issued>
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    <dct:description>&lt;p&gt;Neural machine translation models have shown to achieve high quality when trained and fed with well structured and punctuated input&amp;nbsp;texts. Unfortunately, the latter condition is not met in spoken language translation, where the input is generated by an automatic speech&amp;nbsp;recognition (ASR) system. In this paper, we study how to adapt a strong NMT system to make it robust to typical ASR errors. As in our application scenarios transcripts might be post-edited by human experts, we propose adaptation strategies to train a single system that can translate either clean or noisy input with no supervision on the input type. Our experimental results on a public speech translation data set show that adapting a model on a significant amount of parallel data including ASR transcripts is beneficial with test data of the same type, but produces a small degradation when translating clean text. Adapting on both clean and noisy variants of the same data leads to the best results on both input types.&lt;/p&gt;</dct:description>
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