Learning towards Selective Data Augmentation for Dialogue Generation

TL;DR

This paper introduces a Selective Data Augmentation framework for dialogue generation that intelligently chooses training samples based on quality and representativeness, leading to improved response quality.

Contribution

It proposes a dual adversarial network to select beneficial data points for augmentation, addressing the limitations of existing methods that augment all data indiscriminately.

Findings

Improves response generation performance on DailyDialog and OpenSubtitles datasets.

Effectively selects low-quality and representative data for augmentation.

Enhances various evaluation metrics for dialogue response quality.

Abstract

As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples. However, current data augmentation techniques on the dialog generation task mostly augment all cases in the training dataset without considering the intrinsic attributes between different cases. We argue that not all cases are beneficial for augmentation task, and the cases suitable for augmentation should obey the following two attributes: (1) low-quality (the dialog model cannot generate a high-quality response for the case), (2) representative (the case should represent the property of the whole dataset). Herein, we explore this idea by proposing a Selective Data Augmentation framework (SDA) for the response generation task. SDA employs a dual adversarial network to select the lowest quality and most representative data points for augmentation in one stage. Extensive experiments conducted on two publicly available datasets, i.e., DailyDialog and OpenSubtitles, show that our framework can improve the response generation performance with respect to various metrics.