  Conventional spoken language understanding systems consist of two main
components: an automatic speech recognition module that converts audio to a
transcript, and a natural language understanding module that transforms the
resulting text (or top N hypotheses) into a set of domains, intents, and
arguments. These modules are typically optimized independently. In this paper,
we formulate audio to semantic understanding as a sequence-to-sequence problem
[1]. We propose and compare various encoder-decoder based approaches that
optimize both modules jointly, in an end-to-end manner. Evaluations on a
real-world task show that 1) having an intermediate text representation is
crucial for the quality of the predicted semantics, especially the intent
arguments and 2) jointly optimizing the full system improves overall accuracy
of prediction. Compared to independently trained models, our best jointly
trained model achieves similar domain and intent prediction F1 scores, but
improves argument word error rate by 18% relative.
