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        <namePart type="given">Daniel</namePart>
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            <title>Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)</title>
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        <name type="personal">
            <namePart type="given">Dmitry</namePart>
            <namePart type="family">Ustalov</namePart>
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            <namePart type="given">Swapna</namePart>
            <namePart type="family">Somasundaran</namePart>
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            <namePart type="given">Martin</namePart>
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    <abstract>Speech translation systems usually follow a pipeline approach, using word lattices as an intermediate representation. However, previous work assume access to the original transcriptions used to train the ASR system, which can limit applicability in real scenarios. In this work we propose an approach for speech translation through lattice transformations and neural models based on graph networks. Experimental results show that our approach reaches competitive performance without relying on transcriptions, while also being orders of magnitude faster than previous work.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5304</identifier>
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%0 Conference Proceedings
%T Neural Speech Translation using Lattice Transformations and Graph Networks
%A Beck, Daniel
%A Cohn, Trevor
%A Haffari, Gholamreza
%Y Ustalov, Dmitry
%Y Somasundaran, Swapna
%Y Jansen, Peter
%Y Glavaš, Goran
%Y Riedl, Martin
%Y Surdeanu, Mihai
%Y Vazirgiannis, Michalis
%S Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F beck-etal-2019-neural
%X Speech translation systems usually follow a pipeline approach, using word lattices as an intermediate representation. However, previous work assume access to the original transcriptions used to train the ASR system, which can limit applicability in real scenarios. In this work we propose an approach for speech translation through lattice transformations and neural models based on graph networks. Experimental results show that our approach reaches competitive performance without relying on transcriptions, while also being orders of magnitude faster than previous work.
%R 10.18653/v1/D19-5304
%U https://aclanthology.org/D19-5304
%U https://doi.org/10.18653/v1/D19-5304
%P 26-31

Markdown (Informal)

[Neural Speech Translation using Lattice Transformations and Graph Networks](https://aclanthology.org/D19-5304) (Beck et al., TextGraphs 2019)

• Neural Speech Translation using Lattice Transformations and Graph Networks (Beck et al., TextGraphs 2019)

ACL

• Daniel Beck, Trevor Cohn, and Gholamreza Haffari. 2019. Neural Speech Translation using Lattice Transformations and Graph Networks. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 26–31, Hong Kong. Association for Computational Linguistics.