Multilingual and Cross-Lingual Intent Detection from Spoken Data

Daniela Gerz, Pei-Hao Su, Razvan Kusztos, Avishek Mondal, Michał Lis, Eshan Singhal, Nikola Mrkšić, Tsung-Hsien Wen, Ivan Vulić


Abstract
We present a systematic study on multilingual and cross-lingual intent detection (ID) from spoken data. The study leverages a new resource put forth in this work, termed MInDS-14, a first training and evaluation resource for the ID task with spoken data. It covers 14 intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties. Our key results indicate that combining machine translation models with state-of-the-art multilingual sentence encoders (e.g., LaBSE) yield strong intent detectors in the majority of target languages covered in MInDS-14, and offer comparative analyses across different axes: e.g., translation direction, impact of speech recognition, data augmentation from a related domain. We see this work as an important step towards more inclusive development and evaluation of multilingual ID from spoken data, hopefully in a much wider spectrum of languages compared to prior work.
Anthology ID:
2021.emnlp-main.591
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7468–7475
Language:
URL:
https://aclanthology.org/2021.emnlp-main.591
DOI:
10.18653/v1/2021.emnlp-main.591
Bibkey:
Cite (ACL):
Daniela Gerz, Pei-Hao Su, Razvan Kusztos, Avishek Mondal, Michał Lis, Eshan Singhal, Nikola Mrkšić, Tsung-Hsien Wen, and Ivan Vulić. 2021. Multilingual and Cross-Lingual Intent Detection from Spoken Data. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7468–7475, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Multilingual and Cross-Lingual Intent Detection from Spoken Data (Gerz et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.591.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.591.mp4
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