EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification

Georgios Spithourakis, Ivan Vulić, Michał Lis, Inigo Casanueva, Paweł Budzianowski


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.
Anthology ID:
2022.findings-naacl.124
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1647–1659
Language:
URL:
https://aclanthology.org/2022.findings-naacl.124
DOI:
10.18653/v1/2022.findings-naacl.124
Bibkey:
Cite (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.
Cite (Informal):
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification (Spithourakis et al., Findings 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.findings-naacl.124.pdf
Video:
 https://preview.aclanthology.org/nschneid-patch-1/2022.findings-naacl.124.mp4
Code
 PolyAI-LDN/evi-paper
Data
EVI