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            <namePart type="given">Marie-Catherine</namePart>
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    <abstract>Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E), verify (V), and identify (I) new and recurring users based on their personal information, e.g. postcode, name, and date of birth. In this work, we formalise the three authentication tasks and their evaluation protocols, and we present EVI, a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French. Our proposed models set the first competitive benchmarks, explore the challenges of multilingual natural language processing of spoken dialogue, and set directions for future research.</abstract>
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%0 Conference Proceedings
%T EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification
%A Spithourakis, Georgios
%A Vulić, Ivan
%A Lis, Michał
%A Casanueva, Inigo
%A Budzianowski, Paweł
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F spithourakis-etal-2022-evi
%X Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E), verify (V), and identify (I) new and recurring users based on their personal information, e.g. postcode, name, and date of birth. In this work, we formalise the three authentication tasks and their evaluation protocols, and we present EVI, a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French. Our proposed models set the first competitive benchmarks, explore the challenges of multilingual natural language processing of spoken dialogue, and set directions for future research.
%R 10.18653/v1/2022.findings-naacl.124
%U https://aclanthology.org/2022.findings-naacl.124
%U https://doi.org/10.18653/v1/2022.findings-naacl.124
%P 1647-1659

Markdown (Informal)

[EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification](https://aclanthology.org/2022.findings-naacl.124) (Spithourakis et al., Findings 2022)

• EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification (Spithourakis et al., Findings 2022)

ACL

• Georgios Spithourakis, Ivan Vulić, Michał Lis, Inigo Casanueva, and Paweł Budzianowski. 2022. EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1647–1659, Seattle, United States. Association for Computational Linguistics.