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 homogeneous across languages, 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 can be used to probe LMs capacity to encode complex morphology. Taken together, this work opens up new horizons in the study of computational morphology, leaving ample space for studying neural morphological modeling 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。

无国界的形态学：从子句层面考虑