  Utterance classification performance in low-resource dialogue systems is
constrained by an inevitably high degree of data imbalance in class labels. We
present a new end-to-end pairwise learning framework that is designed
specifically to tackle this phenomenon by inducing a few-shot classification
capability in the utterance representations and augmenting data through an
interpolation of utterance representations. Our approach is a general purpose
training methodology, agnostic to the neural architecture used for encoding
utterances. We show significant improvements in macro-F1 score over standard
cross-entropy training for three different neural architectures, demonstrating
improvements on a Virtual Patient dialogue dataset as well as a low-resourced
emulation of the Switchboard dialogue act classification dataset.
