Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models. (arXiv:2210.15734v1 [cs.CL])

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 …

arxiv labeling language spoken language understanding understanding