Hierarchy is a natural representation of semantic taxonomies, including the
ones routinely used in image segmentation. Indeed, recent work on semantic
segmentation reports improved accuracy from supervised training leveraging
hierarchical label structures. Encouraged by these results, we revisit the
fundamental assumptions behind that work. We postulate and then empirically
verify that the reasons for the observed improvement in segmentation accuracy
may be entirely unrelated to the use of the semantic hierarchy. To demonstrate
this, we design a range of cross-domain experiments with a representative
hierarchical approach. We find that on the new testing domains, a flat
(non-hierarchical) segmentation network, in which the parents are inferred from
the children, has superior segmentation accuracy to the hierarchical approach
across the board. Complementing these findings and inspired by the intrinsic
properties of hyperbolic spaces, we study a more principled approach to
hierarchical segmentation using the Poincar\'e ball model. The hyperbolic
representation largely outperforms the previous (Euclidean) hierarchical
approach as well and is on par with our flat Euclidean baseline in terms of
segmentation accuracy. However, it additionally exhibits surprisingly strong
calibration quality of the parent nodes in the semantic hierarchy, especially
on the more challenging domains. Our combined analysis suggests that the
established practice of hierarchical segmentation may be limited to in-domain
settings, whereas flat classifiers generalize substantially better, especially
if they are modeled in the hyperbolic space.

本文通过多个跨领域实验，重新评估了语义分割中使用层次结构的有效性，发现在新的测试领域中，扁平化的分割网络优于层次结构方法，并且通过使用 Poincaré 球模型，超几何空间的表征方法在分割精度上显著优于传统的欧氏空间层次结构方法，在父节点的校准质量上表现出出人意料的强大性能。因此，综合分析指出，在领域之间的应用中，传统的层次结构分割方法可能受到限制，而扁平化的分类器在超几何空间下具有更好的泛化能力。

在 Poincaré Ball 中进行层次语义分割：降低父节点偏见

Flattening the Parent Bias: Hierarchical Semantic Segmentation in the  Poincaré Ball

Being able to extract from scientific papers their main points, key insights,
and other important information, referred to here as aspects, might facilitate
the process of conducting a scientific literature review. Therefore, the aim of
our research is to create a tool for automatic aspect extraction from
Russian-language scientific texts of any domain. In this paper, we present a
cross-domain dataset of scientific texts in Russian, annotated with such
aspects as Task, Contribution, Method, and Conclusion, as well as a baseline
algorithm for aspect extraction, based on the multilingual BERT model
fine-tuned on our data. We show that there are some differences in aspect
representation in different domains, but even though our model was trained on a
limited number of scientific domains, it is still able to generalize to new
domains, as was proved by cross-domain experiments. The code and the dataset
are available at
https://github.com/anna-marshalova/automatic-aspect-extraction-from-scientific-texts.

我们的研究旨在创建一个用于从任何领域的俄语科技文本中自动提取方面的工具。本文提供了一份跨领域的俄文科技文本数据集，标注了任务、贡献、方法和结论等方面，并基于经过细调的多语言 BERT 模型提出了一个基准算法用于方面提取。我们展示了不同领域中方面表征存在一些差异，但即使我们的模型仅在有限数量的科技领域进行了训练，它仍能够推广到新的领域，这一点已通过跨领域实验证明。代码和数据集可在 https://github.com/anna-marshalova/automatic-aspect-extraction-from-scientific-texts 上获得。