In this article we present Enhanced Rhetorical Structure Theory (eRST), a new
theoretical framework for computational discourse analysis, based on an
expansion of Rhetorical Structure Theory (RST). The framework encompasses
discourse relation graphs with tree-breaking, nonprojective and concurrent
relations, as well as implicit and explicit signals which give explainable
rationales to our analyses. We survey shortcomings of RST and other existing
frameworks, such as Segmented Discourse Representation Theory (SDRT), the Penn
Discourse Treebank (PDTB) and Discourse Dependencies, and address these using
constructs in the proposed theory. We provide annotation, search and
visualization tools for data, and present and evaluate a freely available
corpus of English annotated according to our framework, encompassing 12 spoken
and written genres with over 200K tokens. Finally, we discuss automatic
parsing, evaluation metrics and applications for data in our framework.

我们提出了增强修辞结构理论（eRST），这是一种基于修辞结构理论（RST）扩展的计算化话语分析的新理论框架。该框架包括具有树破碎、非投射和并发关系的话语关系图，以及提供解释性的隐式和显式信号来解释我们的分析。我们调查了 RST 和其他现有框架的缺点，如分段话语表示理论（SDRT）、宾夕法尼亚话语树库（PDTB）和话语依赖，并使用所提出的理论中的构造来解决这些问题。我们为数据提供注释、搜索和可视化工具，并介绍和评估了一个根据我们的框架进行注释的英语语料库，包括 12 种口语和书面体样式，200,000 个单词。最后，我们讨论了在我们的框架中的数据自动解析、评估指标和应用。

eRST：话语关系和组织的信号图理论

eRST: A Signaled Graph Theory of Discourse Relations and Organization

Automatic parsing of anatomical objects in X-ray images is critical to many
clinical applications in particular towards image-guided invention and workflow
automation. Existing deep network models require a large amount of labeled
data. However, obtaining accurate pixel-wise labeling in X-ray images relies
heavily on skilled clinicians due to the large overlaps of anatomy and the
complex texture patterns. On the other hand, organs in 3D CT scans preserve
clearer structures as well as sharper boundaries and thus can be easily
delineated. In this paper, we propose a novel model framework for learning
automatic X-ray image parsing from labeled CT scans. Specifically, a Dense
Image-to-Image network (DI2I) for multi-organ segmentation is first trained on
X-ray like Digitally Reconstructed Radiographs (DRRs) rendered from 3D CT
volumes. Then we introduce a Task Driven Generative Adversarial Network
(TD-GAN) architecture to achieve simultaneous style transfer and parsing for
unseen real X-ray images. TD-GAN consists of a modified cycle-GAN substructure
for pixel-to-pixel translation between DRRs and X-ray images and an added
module leveraging the pre-trained DI2I to enforce segmentation consistency. The
TD-GAN framework is general and can be easily adapted to other learning tasks.
In the numerical experiments, we validate the proposed model on 815 DRRs and
153 topograms. While the vanilla DI2I without any adaptation fails completely
on segmenting the topograms, the proposed model does not require any topogram
labels and is able to provide a promising average dice of 85% which achieves
the same level accuracy of supervised training (88%).

本文提出了一种通过学习自动解析 3D CT 扫描并将其应用于 X 光图像中的新模型框架 DI2I 和 TD-GAN，该框架在未见过的真实 X 光图像上实现了同时样式转换和解析的效果，且不需要 Topogram 标签即可实现 85% 的平均 Dice 精度，与有监督训练的 88% 相当。