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.

介绍一种选择性数据增强框架（SDA），它利用对抗网络选择哪些数据点适合用于增强神经对话模型的性能，从而改善响应生成性能。