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%.

使用预训练模型和多语言设置，提出了一种统一的方法来进行 End-to-End 语音语言理解，包括词槽填充，通过在可用的大规模语音识别数据上进行预训练，该方法在多个数据集和跨语言任务上取得了显著的性能提升。