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Safe In-vehicle Dialogue Using Learned Predictions of User Utterances
Talkamatic AB, Gothenburg, Sweden.
Talkamatic AB, Gothenburg, Sweden.
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-2791-6647
2014 (English)In: EACL 2014 - Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics / [ed] Shuly Wintner, Marko Tadić, Bogdan Babych, Association for Computational Linguistics, 2014, p. 37-40Conference paper, Published paper (Refereed)
Abstract [en]

We present a multimodal in-vehicle dialogue system which uses learned predictions of user answers to enable shorter, more efficient, and thus safer natural language dialogues. © 2014 Association for Computational Linguistics

Place, publisher, year, edition, pages
Association for Computational Linguistics, 2014. p. 37-40
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-51370DOI: 10.3115/v1/E14-2010Scopus ID: 2-s2.0-85122020941ISBN: 9781937284756 (print)OAI: oai:DiVA.org:hh-51370DiVA, id: diva2:1787498
Conference
14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, Gothenburg, Sweden, 26-30 April, 2014
Projects
Safe Speech by Knowledge
Funder
Vinnova
Note

Funding: This work was carried out within the FFI project “Safe Speech by Knowledge” (2012-00941), funded by VINNOVA, Volvo Car Corporation and Talkamatic.

Available from: 2023-08-14 Created: 2023-08-14 Last updated: 2023-08-14Bibliographically approved

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Wärnestål, Pontus

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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