  Transformer networks and self-supervised pre-training have consistently
delivered state-of-art results in the field of natural language processing
(NLP); however, their merits in the field of spoken language understanding
(SLU) still need further investigation. In this paper we introduce a modular
End-to-End (E2E) SLU transformer network based architecture which allows the
use of self-supervised pre-trained acoustic features, pre-trained model
initialization and multi-task training. Several SLU experiments for predicting
intent and entity labels/values using the ATIS dataset are performed. These
experiments investigate the interaction of pre-trained model initialization and
multi-task training with either traditional filterbank or self-supervised
pre-trained acoustic features. Results show not only that self-supervised
pre-trained acoustic features outperform filterbank features in almost all the
experiments, but also that when these features are used in combination with
multi-task training, they almost eliminate the necessity of pre-trained model
initialization.
