Handling Class Imbalance in Low-Resource Dialogue Systems by Combining Few-Shot Classification and Interpolation

• URL: http://arxiv.org/abs/2010.15090v1
• Date: Wed, 28 Oct 2020 17:05:24 GMT
• Title: Handling Class Imbalance in Low-Resource Dialogue Systems by Combining Few-Shot Classification and Interpolation
• Authors: Vishal Sunder and Eric Fosler-Lussier
• Abstract summary: 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 inducing a few-shot classification capability in the utterance representations and augmenting data through an agnostic of utterance representations.
• Score: 19.988400064884825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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