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    <titleInfo>
        <title>Direct Speech Translation for Automatic Subtitling</title>
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    <name type="personal">
        <namePart type="given">Sara</namePart>
        <namePart type="family">Papi</namePart>
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    <name type="personal">
        <namePart type="given">Alina</namePart>
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    <name type="personal">
        <namePart type="given">Mauro</namePart>
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    <name type="personal">
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    <abstract>Automatic subtitling is the task of automatically translating the speech of audiovisual content into short pieces of timed text, i.e., subtitles and their corresponding timestamps. The generated subtitles need to conform to space and time requirements, while being synchronized with the speech and segmented in a way that facilitates comprehension. Given its considerable complexity, the task has so far been addressed through a pipeline of components that separately deal with transcribing, translating, and segmenting text into subtitles, as well as predicting timestamps. In this paper, we propose the first direct speech translation model for automatic subtitling that generates subtitles in the target language along with their timestamps with a single model. Our experiments on 7 language pairs show that our approach outperforms a cascade system in the same data condition, also being competitive with production tools on both in-domain and newly released out-domain benchmarks covering new scenarios.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00607</identifier>
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        <date>2023</date>
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%0 Journal Article
%T Direct Speech Translation for Automatic Subtitling
%A Papi, Sara
%A Gaido, Marco
%A Karakanta, Alina
%A Cettolo, Mauro
%A Negri, Matteo
%A Turchi, Marco
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F papi-etal-2023-direct-speech
%X Automatic subtitling is the task of automatically translating the speech of audiovisual content into short pieces of timed text, i.e., subtitles and their corresponding timestamps. The generated subtitles need to conform to space and time requirements, while being synchronized with the speech and segmented in a way that facilitates comprehension. Given its considerable complexity, the task has so far been addressed through a pipeline of components that separately deal with transcribing, translating, and segmenting text into subtitles, as well as predicting timestamps. In this paper, we propose the first direct speech translation model for automatic subtitling that generates subtitles in the target language along with their timestamps with a single model. Our experiments on 7 language pairs show that our approach outperforms a cascade system in the same data condition, also being competitive with production tools on both in-domain and newly released out-domain benchmarks covering new scenarios.
%R 10.1162/tacl_a_00607
%U https://aclanthology.org/2023.tacl-1.77/
%U https://doi.org/10.1162/tacl_a_00607
%P 1355-1376

Markdown (Informal)

[Direct Speech Translation for Automatic Subtitling](https://aclanthology.org/2023.tacl-1.77/) (Papi et al., TACL 2023)

• Direct Speech Translation for Automatic Subtitling (Papi et al., TACL 2023)

ACL

• Sara Papi, Marco Gaido, Alina Karakanta, Mauro Cettolo, Matteo Negri, and Marco Turchi. 2023. Direct Speech Translation for Automatic Subtitling. Transactions of the Association for Computational Linguistics, 11:1355–1376.