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Transfer Learning of Transformers for Spoken Language Understanding

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43965700" target="_blank" >RIV/49777513:23520/22:43965700 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/chapter/10.1007/978-3-031-16270-1_40" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-16270-1_40</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-031-16270-1_40" target="_blank" >10.1007/978-3-031-16270-1_40</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Transfer Learning of Transformers for Spoken Language Understanding

  • Original language description

    Pre-trained models used in the transfer-learning scenario are recently becoming very popular. Such models benefit from the availability of large sets of unlabeled data. Two kinds of such models include the Wav2Vec 2.0 speech recognizer and T5 text-to-text transformer. In this paper, we describe a novel application of such models for dialog systems, where both the speech recognizer and the spoken language understanding modules are represented as Transformer models. Such composition outperforms the baseline based on the DNN-HMM speech recognizer and CNN understanding.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20205 - Automation and control systems

Result continuities

  • Project

    <a href="/en/project/GA22-27800S" target="_blank" >GA22-27800S: Transformers of multiple modalities for more natural spoken dialog</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Text, Speech, and Dialogue 25th International Conference, TSD 2022, Brno, Czech Republic, September 6–9, 2022, Proceedings

  • ISBN

    978-3-031-16269-5

  • ISSN

    0302-9743

  • e-ISSN

    1611-3349

  • Number of pages

    12

  • Pages from-to

    489-500

  • Publisher name

    Springer International Publishing

  • Place of publication

    Cham

  • Event location

    Brno, Czech Republic

  • Event date

    Sep 6, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article