  Spoken language understanding (SLU) treats automatic speech recognition (ASR)
and natural language understanding (NLU) as a unified task and usually suffers
from data scarcity. We exploit an ASR and NLU joint training method based on
meta auxiliary learning to improve the performance of low-resource SLU task by
only taking advantage of abundant manual transcriptions of speech data. One
obvious advantage of such method is that it provides a flexible framework to
implement a low-resource SLU training task without requiring access to any
further semantic annotations. In particular, a NLU model is taken as label
generation network to predict intent and slot tags from texts; a multi-task
network trains ASR task and SLU task synchronously from speech; and the
predictions of label generation network are delivered to the multi-task network
as semantic targets. The efficiency of the proposed algorithm is demonstrated
with experiments on the public CATSLU dataset, which produces more suitable ASR
hypotheses for the downstream NLU task.
