  End-to-end spoken language understanding (SLU) systems are gaining popularity
over cascaded approaches due to their simplicity and ability to avoid error
propagation. However, these systems model sequence labeling as a sequence
prediction task causing a divergence from its well-established token-level
tagging formulation. We build compositional end-to-end SLU systems that
explicitly separate the added complexity of recognizing spoken mentions in SLU
from the NLU task of sequence labeling. By relying on intermediate decoders
trained for ASR, our end-to-end systems transform the input modality from
speech to token-level representations that can be used in the traditional
sequence labeling framework. This composition of ASR and NLU formulations in
our end-to-end SLU system offers direct compatibility with pre-trained ASR and
NLU systems, allows performance monitoring of individual components and enables
the use of globally normalized losses like CRF, making them attractive in
practical scenarios. Our models outperform both cascaded and direct end-to-end
models on a labeling task of named entity recognition across SLU benchmarks.
