Multilingual and Cross-Lingual Intent Detection from Spoken Data

By Daniela Gerz, Pei-hao Su, Razvan Kusztos, Avishek Mondal, M. Lis, Eshan Singhal, Nikola Mrksic, Tsung-Hsien Wen, Ivan Vulic. 2021

52 citations recorded by the source corpus.

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.

Subfields: Natural Language Processing, Speech & Audio

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Citations in the corpus

mSLAM: Massively multilingual joint pre-training for speech and text (2022, methodology): “2) and Minds-14 speech intent classification (Gerz et al., 2021), mSLAM demonstrates significant quality improvements over equivalent models trained only on speech.”
XTREME-S: Evaluating Cross-lingual Speech Representations (2022, background): “Minds-14 MINDS-14 [39] is an intent classification task from spoken data.”