  End-to-end spoken language understanding (SLU) systems have many advantages
over conventional pipeline systems, but collecting in-domain speech data to
train an end-to-end system is costly and time consuming. One question arises
from this: how to train an end-to-end SLU with limited amounts of data? Many
researchers have explored approaches that make use of other related data
resources, typically by pre-training parts of the model on high-resource speech
recognition. In this paper, we suggest improving the generalization performance
of SLU models with a non-standard learning algorithm, Reptile. Though Reptile
was originally proposed for model-agnostic meta learning, we argue that it can
also be used to directly learn a target task and result in better
generalization than conventional gradient descent. In this work, we employ
Reptile to the task of end-to-end spoken intent classification. Experiments on
four datasets of different languages and domains show improvement of intent
prediction accuracy, both when Reptile is used alone and used in addition to
pre-training.
