Previous studies have shown that leveraging domain index can significantly
boost domain adaptation performance (arXiv:2007.01807, arXiv:2202.03628).
However, such domain indices are not always available. To address this
challenge, we first provide a formal definition of domain index from the
probabilistic perspective, and then propose an adversarial variational Bayesian
framework that infers domain indices from multi-domain data, thereby providing
additional insight on domain relations and improving domain adaptation
performance. Our theoretical analysis shows that our adversarial variational
Bayesian framework finds the optimal domain index at equilibrium. Empirical
results on both synthetic and real data verify that our model can produce
interpretable domain indices which enable us to achieve superior performance
compared to state-of-the-art domain adaptation methods. Code is available at
this https URL.

本文提出了一种通过 Adversarial Variational Bayesian 方法从多域数据中推断域索引的框架，旨在提高域自适应性能并提供关于域之间关系的额外洞察力，实验结果表明该模型可以生成可解释的域指数，相比其他域自适应方法，在合成和真实数据上均能实现更优异的性能。

领域索引变分贝叶斯：可解释性领域索引用于领域自适应

Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation

In order to alleviate the shortage of multi-domain data and to capture
discourse phenomena for task-oriented dialogue modeling, we propose RiSAWOZ, a
large-scale multi-domain Chinese Wizard-of-Oz dataset with Rich Semantic
Annotations. RiSAWOZ contains 11.2K human-to-human (H2H) multi-turn
semantically annotated dialogues, with more than 150K utterances spanning over
12 domains, which is larger than all previous annotated H2H conversational
datasets. Both single- and multi-domain dialogues are constructed, accounting
for 65% and 35%, respectively. Each dialogue is labeled with comprehensive
dialogue annotations, including dialogue goal in the form of natural language
description, domain, dialogue states and acts at both the user and system side.
In addition to traditional dialogue annotations, we especially provide
linguistic annotations on discourse phenomena, e.g., ellipsis and coreference,
in dialogues, which are useful for dialogue coreference and ellipsis resolution
tasks. Apart from the fully annotated dataset, we also present a detailed
description of the data collection procedure, statistics and analysis of the
dataset. A series of benchmark models and results are reported, including
natural language understanding (intent detection & slot filling), dialogue
state tracking and dialogue context-to-text generation, as well as coreference
and ellipsis resolution, which facilitate the baseline comparison for future
research on this corpus.

本研究提出了 RiSAWOZ，这是一个大规模的、多领域的中文人机对话数据集，并提供了丰富的语义注释和对话自然语言描述，可用于对话系统的基准比较和未来研究的任务导向对话建模。

RiSAWOZ: 大规模多领域 Wizard-of-Oz 数据集，具有丰富的语义注释，用于面向任务的对话建模

RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich  Semantic Annotations for Task-Oriented Dialogue Modeling

Multi-domain data are widely leveraged in vision applications taking
advantage of complementary information from different modalities, e.g., brain
tumor segmentation from multi-parametric magnetic resonance imaging (MRI).
However, due to possible data corruption and different imaging protocols, the
availability of images for each domain could vary amongst multiple data sources
in practice, which makes it challenging to build a universal model with a
varied set of input data. To tackle this problem, we propose a general approach
to complete the random missing domain(s) data in real applications.
Specifically, we develop a novel multi-domain image completion method that
utilizes a generative adversarial network (GAN) with a representational
disentanglement scheme to extract shared skeleton encoding and separate flesh
encoding across multiple domains. We further illustrate that the learned
representation in multi-domain image completion could be leveraged for
high-level tasks, e.g., segmentation, by introducing a unified framework
consisting of image completion and segmentation with a shared content encoder.
The experiments demonstrate consistent performance improvement on three
datasets for brain tumor segmentation, prostate segmentation, and facial
expression image completion respectively.

本文提出了一种基于生成对抗网络 (GAN) 的多领域图像完成方法，该方法使用表示学习技术在多个领域中提取共享骨架编码和分离的肉体编码，以完成实际应用中可能出现的领域数据缺失问题，并证明所学习的表示方法对高级任务具有潜在价值。