The task of topical segmentation is well studied, but previous work has
mostly addressed it in the context of structured, well-defined segments, such
as segmentation into paragraphs, chapters, or segmenting text that originated
from multiple sources. We tackle the task of segmenting running (spoken)
narratives, which poses hitherto unaddressed challenges. As a test case, we
address Holocaust survivor testimonies, given in English. Other than the
importance of studying these testimonies for Holocaust research, we argue that
they provide an interesting test case for topical segmentation, due to their
unstructured surface level, relative abundance (tens of thousands of such
testimonies were collected), and the relatively confined domain that they
cover. We hypothesize that boundary points between segments correspond to low
mutual information between the sentences proceeding and following the boundary.
Based on this hypothesis, we explore a range of algorithmic approaches to the
task, building on previous work on segmentation that uses generative Bayesian
modeling and state-of-the-art neural machinery. Compared to manually annotated
references, we find that the developed approaches show considerable
improvements over previous work.

本文研究如何将长篇英语口述叙述中的段落进行主题划分，以大量收集的 Holocaust 幸存者证言为例，通过使用诸如贝叶斯模型和神经算法等算法，成功地将其与手动标记参考资料做了比较，展示了比以往研究更高的水平。

口述叙述的主题划分：以大屠杀幸存者证言为例的测试

Topical Segmentation of Spoken Narratives: A Test Case on Holocaust Survivor Testimonies

Advancements in neural machinery have led to a wide range of algorithmic
solutions for molecular property prediction. Two classes of models in
particular have yielded promising results: neural networks applied to computed
molecular fingerprints or expert-crafted descriptors, and graph convolutional
neural networks that construct a learned molecular representation by operating
on the graph structure of the molecule. However, recent literature has yet to
clearly determine which of these two methods is superior when generalizing to
new chemical space. Furthermore, prior research has rarely examined these new
models in industry research settings in comparison to existing employed models.
In this paper, we benchmark models extensively on 19 public and 16 proprietary
industrial datasets spanning a wide variety of chemical endpoints. In addition,
we introduce a graph convolutional model that consistently matches or
outperforms models using fixed molecular descriptors as well as previous graph
neural architectures on both public and proprietary datasets. Our empirical
findings indicate that while approaches based on these representations have yet
to reach the level of experimental reproducibility, our proposed model
nevertheless offers significant improvements over models currently used in
industrial workflows.

在分子属性预测的算法解决方案中，神经机器技术的进步导致了一系列算法解决方案，其中神经网络应用于计算分子指纹或专家制作的描述符以及构造学习分子表示的图卷积神经网络最为有效，这项研究进行了广泛的基准测试，并提出了优于现有模型的图卷积模型的实证发现。