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
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.
BibTeX
@inproceedings{gerz-etal-2021-multilingual,
title = "Multilingual and Cross-Lingual Intent Detection from Spoken Data",
author = "Gerz, Daniela and
Su, Pei-Hao and
Kusztos, Razvan and
Mondal, Avishek and
Lis, Micha{\l} and
Singhal, Eshan and
Mrk{\v{s}}i{\'c}, Nikola and
Wen, Tsung-Hsien and
Vuli{\'c}, Ivan",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.591/",
doi = "10.18653/v1/2021.emnlp-main.591",
pages = "7468--7475"
}