It has been exactly a decade since the first establishment of SPMRL, a
research initiative unifying multiple research efforts to address the peculiar
challenges of Statistical Parsing for Morphologically-Rich Languages
(MRLs).Here we reflect on parsing MRLs in that decade, highlight the solutions
and lessons learned for the architectural, modeling and lexical challenges in
the pre-neural era, and argue that similar challenges re-emerge in neural
architectures for MRLs. We then aim to offer a climax, suggesting that
incorporating symbolic ideas proposed in SPMRL terms into nowadays neural
architectures has the potential to push NLP for MRLs to a new level. We sketch
strategies for designing Neural Models for MRLs (NMRL), and showcase
preliminary support for these strategies via investigating the task of
multi-tagging in Hebrew, a morphologically-rich, high-fusion, language

该研究回顾了过去十年在 SPMRL 框架下解析富形态语言的建模、词汇和架构方面所遇到的挑战，并提出将 SPMRL 的符号思想引入现代神经结构设计的思路，从而推动 NMRL（神经模型富形态语言）研究进入新的领域，同时展示了在希伯来语的多标记任务中设计神经模型的初步支持。

从 SPMRL 到 NMRL：在十年解析形态丰富的语言（MRLs）中我们学到了什么（又忘记了什么）？

From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of  Parsing Morphologically-Rich Languages (MRLs)?

Automated whole slide image (WSI) tagging has become a growing demand due to
the increasing volume and diversity of WSIs collected nowadays in
histopathology. Various methods have been studied to classify WSIs with single
tags but none of them focuses on labeling WSIs with multiple tags. To this end,
we propose a novel end-to-end trainable deep neural network named Patch
Transformer which can effectively predict multiple slide-level tags from WSI
patches based on both the correlations and the uniqueness between the tags.
Specifically, the proposed method learns patch characteristics considering 1)
patch-wise relations through a patch transformation module and 2) tag-wise
uniqueness for each tagging task through a multi-tag attention module.
Extensive experiments on a large and diverse dataset consisting of 4,920 WSIs
prove the effectiveness of the proposed model.

提出了一种利用 Patch Transformer 来进行多标签预测的深度神经网络模型，可以从组织病理学切片图像中有效地预测多个幻灯片级标签，并通过大量实验证明了该模型的有效性。