Leveraging Multilingual Self-Supervised Pretrained Models for Sequence-to-Sequence End-to-End Spoken Language Understanding

TL;DR

This paper introduces a multilingual end-to-end spoken language understanding model that leverages pretrained speech and text models, demonstrating significant improvements across multiple datasets and languages, especially with extensive multilingual pretraining.

Contribution

The work presents a unified multilingual E2E-SLU approach integrating pretrained models and shows how multilingual pretraining enhances performance and cross-lingual transfer capabilities.

Findings

Outperforms state-of-the-art on two SLU datasets

Achieves near state-of-the-art on additional datasets

Improves cross-lingual results on PortMEDIA-Language dataset

Abstract

A number of methods have been proposed for End-to-End Spoken Language Understanding (E2E-SLU) using pretrained models, however their evaluation often lacks multilingual setup and tasks that require prediction of lexical fillers, such as slot filling. In this work, we propose a unified method that integrates multilingual pretrained speech and text models and performs E2E-SLU on six datasets in four languages in a generative manner, including the prediction of lexical fillers. We investigate how the proposed method can be improved by pretraining on widely available speech recognition data using several training objectives. Pretraining on 7000 hours of multilingual data allows us to outperform the state-of-the-art ultimately on two SLU datasets and partly on two more SLU datasets. Finally, we examine the cross-lingual capabilities of the proposed model and improve on the best known result on the PortMEDIA-Language dataset by almost half, achieving a Concept/Value Error Rate of 23.65%.