Recent works have shown that generative data augmentation, where synthetic
samples generated from deep generative models complement the training dataset,
benefit NLP tasks. In this work, we extend this approach to the task of dialog
state tracking for goal-oriented dialogs. Due to the inherent hierarchical
structure of goal-oriented dialogs over utterances and related annotations, the
deep generative model must be capable of capturing the coherence among
different hierarchies and types of dialog features. We propose the Variational
Hierarchical Dialog Autoencoder (VHDA) for modeling the complete aspects of
goal-oriented dialogs, including linguistic features and underlying structured
annotations, namely speaker information, dialog acts, and goals. The proposed
architecture is designed to model each aspect of goal-oriented dialogs using
inter-connected latent variables and learns to generate coherent goal-oriented
dialogs from the latent spaces. To overcome training issues that arise from
training complex variational models, we propose appropriate training
strategies. Experiments on various dialog datasets show that our model improves
the downstream dialog trackers' robustness via generative data augmentation. We
also discover additional benefits of our unified approach to modeling
goal-oriented dialogs: dialog response generation and user simulation, where
our model outperforms previous strong baselines.

本文提出了一种名为 Variational Hierarchical Dialog Autoencoder（VHDA）的深度生成模型，用于 goal-oriented dialogs 的任务，它有效地捕获了不同层次和类型的 dialog features 之间的一致性，并且使用生成性数据增强方案，提高了下游目标跟踪器的鲁棒性，同时也在 dialog response generation 和 user simulation 方面具有良好表现。

基于变分分层对话自编码器的对话状态跟踪数据增强

Variational Hierarchical Dialog Autoencoder for Dialog State Tracking  Data Augmentation

Most existing works on dialog systems only consider conversation content
while neglecting the personality of the user the bot is interacting with, which
begets several unsolved issues. In this paper, we present a personalized
end-to-end model in an attempt to leverage personalization in goal-oriented
dialogs. We first introduce a Profile Model which encodes user profiles into
distributed embeddings and refers to conversation history from other similar
users. Then a Preference Model captures user preferences over knowledge base
entities to handle the ambiguity in user requests. The two models are combined
into the Personalized MemN2N. Experiments show that the proposed model achieves
qualitative performance improvements over state-of-the-art methods. As for
human evaluation, it also outperforms other approaches in terms of task
completion rate and user satisfaction.

本文提出一种个性化的端到端模型，以应用个性化技术来解决对话系统中的一些悬而未决的问题。该模型结合了 “个人资料模型” 和 “偏好模型” 两个子模型，并实践证明其在对话系统任务完成率和用户满意度等方面取得了良好表现。