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Restoring Punctuation and Capitalization Using Transformer Models

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Statistical Language and Speech Processing (SLSP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11171))

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Abstract

Restoring punctuation and capitalization in the output of automatic speech recognition (ASR) system greatly improves readability and extends the number of downstream applications. We present a Transformer-based method for restoring punctuation and capitalization for Latvian and English, following the established approach of using neural machine translation (NMT) models. NMT methods here pose a challenge as the length of the predicted sequence does not always match the length of the input sequence. We offer two solutions to this problem: a simple target sequence cutting or padding by force and a more sophisticated attention alignment-based method. Our approach reaches new state of the art results for Latvian and competitive results on English.

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Notes

  1. 1.

    Europarl results were not in print version of the paper, but they can be found at https://github.com/ottokart/punctuator2.

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Acknowledgements

The research has been supported by the European Regional Development Fund within the project “Neural Network Modelling for Inflected Natural Languages” No. 1.1.1.1/16/A/215.

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Authors and Affiliations

  1. Tilde, Vienibas gatve 75A, Riga, Latvia

    Andris Vāravs & Askars Salimbajevs

Authors
  1. Andris Vāravs
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  2. Askars Salimbajevs
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Correspondence to Andris Vāravs .

Editor information

Editors and Affiliations

  1. University of Mons, Mons, Belgium

    Thierry Dutoit

  2. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  3. University of Mons, Mons, Belgium

    Gueorgui Pironkov

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Vāravs, A., Salimbajevs, A. (2018). Restoring Punctuation and Capitalization Using Transformer Models. In: Dutoit, T., Martín-Vide, C., Pironkov, G. (eds) Statistical Language and Speech Processing. SLSP 2018. Lecture Notes in Computer Science(), vol 11171. Springer, Cham. https://doi.org/10.1007/978-3-030-00810-9_9

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  • DOI: https://doi.org/10.1007/978-3-030-00810-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00809-3

  • Online ISBN: 978-3-030-00810-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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