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。

面向对话生成的选择性数据增强学习

Learning towards Selective Data Augmentation for Dialogue Generation

Recently, topic-grounded dialogue system has attracted significant attention
due to its effectiveness in predicting the next topic to yield better responses
via the historical context and given topic sequence. However, almost all
existing topic prediction solutions focus on only the current conversation and
corresponding topic sequence to predict the next conversation topic, without
exploiting other topic-guided conversations which may contain relevant
topic-transitions to current conversation. To address the problem, in this
paper we propose a novel approach, named Sequential Global Topic Attention
(SGTA) to exploit topic transition over all conversations in a subtle way for
better modeling post-to-response topic-transition and guiding the response
generation to the current conversation. Specifically, we introduce a latent
space modeled as a Multivariate Skew-Normal distribution with hybrid kernel
functions to flexibly integrate the global-level information with
sequence-level information, and predict the topic based on the distribution
sampling results. We also leverage a topic-aware prior-posterior approach for
secondary selection of predicted topics, which is utilized to optimize the
response generation task. Extensive experiments demonstrate that our model
outperforms competitive baselines on prediction and generation tasks.

本文提出一种名为 Sequential Global Topic Attention（SGTA）的新方法，将所有对话中主题转换的全局信息柔和地利用起来，以更好地建模以及引导当前对话的回复生成，采用与混合内核函数建模的多变量偏态正态分布的潜在空间预测主题，并利用主题感知的先验 - 后验方法进行次要预测主题的选择，实验证明 SGTA 方法在预测和生成任务上优于竞争基准。