Morphological tasks use large multi-lingual datasets that organize words into
inflection tables, which then serve as training and evaluation data for various
tasks. However, a closer inspection of these data reveals profound
cross-linguistic inconsistencies, that arise from the lack of a clear
linguistic and operational definition of what is a word, and that severely
impair the universality of the derived tasks. To overcome this deficiency, we
propose to view morphology as a clause-level phenomenon, rather than
word-level. It is anchored in a fixed yet inclusive set of features, that
encapsulates all functions realized in a saturated clause. We deliver
MightyMorph, a novel dataset for clause-level morphology covering 4
typologically-different languages: English, German, Turkish and Hebrew. We use
this dataset to derive 3 clause-level morphological tasks: inflection,
reinflection and analysis. Our experiments show that the clause-level tasks are
substantially harder than the respective word-level tasks, while having
comparable complexity across languages. Furthermore, redefining morphology to
the clause-level provides a neat interface with contextualized language models
(LMs) and allows assessing the morphological knowledge encoded in these models
and their usability for morphological tasks. Taken together, this work opens up
new horizons in the study of computational morphology, leaving ample space for
studying neural morphology cross-linguistically.

本文通过提出一种基于句子层面的形态学，并创建了一个新的、基于句子层面的多语言数据集 MightyMorph，得出基于句子层面的变形、重新变形和分析任务比基于单词水平的任务更具挑战性，同时提供一种方便的界面与语境化语言模型（LMs）相接，评估这些模型中编码的形态学知识和它们用于形态学任务的可用性，为跨语言神经形态的研究打开了新的视野。