无边界形态学：小句级形态学
Morphology Without Borders: Clause-Level Morphology
摘要：
形态学任务使用大型多语言数据集，将单词组织成变形表，然后作为各种任务的训练和评估数据。然而，对这些数据的仔细检查揭示了深刻的跨语言不一致，这是由于缺乏一个明确的语言和操作定义什么是一个词，并严重损害了衍生任务的普遍性。为了克服这一缺陷，我们建议将形态学视为小句层面的现象，而不是词层面的现象。它锚定在一个固定但包容的功能集，封装在饱和子句中实现的所有功能。我们提供了MightyMorph，一个用于子句级形态的新数据集，涵盖4种类型不同的语言：英语，德语，土耳其语和希伯来语。我们使用这个数据集派生出3个小句级别的形态学任务：变形，再变形和分析。我们的实验表明，小句级别的任务比相应的单词级别的任务要难得多，同时在不同的语言中具有相当的复杂性。此外，重新定义形态的子句级别提供了一个整洁的接口与上下文语言模型（LM），并允许评估这些模型中编码的形态知识和它们的可用性形态任务。总之，这项工作开辟了计算形态学研究的新视野，为跨语言研究神经形态学留下了充足的空间。
英文摘要：
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.
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