A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification

TL;DR

This paper investigates feature space data augmentation techniques to enhance few-shot intent classification in conversational AI, demonstrating their effectiveness over traditional transfer learning on public datasets.

Contribution

It introduces and evaluates six feature space augmentation methods combined with BERT for few-shot intent classification, highlighting simple yet effective techniques.

Findings

Upsampling in latent space is a strong baseline.

Adding the difference between examples improves performance.

Feature space augmentation outperforms traditional transfer learning.

Abstract

New conversation topics and functionalities are constantly being added to conversational AI agents like Amazon Alexa and Apple Siri. As data collection and annotation is not scalable and is often costly, only a handful of examples for the new functionalities are available, which results in poor generalization performance. We formulate it as a Few-Shot Integration (FSI) problem where a few examples are used to introduce a new intent. In this paper, we study six feature space data augmentation methods to improve classification performance in FSI setting in combination with both supervised and unsupervised representation learning methods such as BERT. Through realistic experiments on two public conversational datasets, SNIPS, and the Facebook Dialog corpus, we show that data augmentation in feature space provides an effective way to improve intent classification performance in few-shot setting beyond traditional transfer learning approaches. In particular, we show that (a) upsampling in latent space is a competitive baseline for feature space augmentation (b) adding the difference between two examples to a new example is a simple yet effective data augmentation method.