Morphology Without Borders: Clause-Level Morphology

Morphology Without Borders: Clause-Level Morphology Omer Goldman Bar Ilan University, Israel omer.goldman@gmail.com Reut Tsarfaty Bar Ilan University, Israel reut.tsarfaty@biu.ac.il Abstract Morphological tasks use large multi-lingual datasets that organize words into inflection tables, which then serve as training and eval- uation data for various tasks. However, a closer inspection of these data reveals pro- found cross-linguistic inconsistencies, which arise from the lack of a clear linguistic and operational definition of what is a word, and which 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 an- chored 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 hav- ing 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 knowl- edge encoded in these models and their usabil- ity 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. 1 Introduction Morphology has long been viewed as a funda- mental part of NLP, especially in cross-lingual settings—from translation (Minkov et al., 2007; Chahuneau et al., 2013) to sentiment analy- sis (Abdul-Mageed et al., 2011; Amram et al., 2018)—as languages vary wildly in the extent to which they use morphological marking as a means to realize meanings. Recent years have seen a tremendous devel- opment in the data available for supervised mor- phological tasks, mostly via UniMorph (Batsuren et al., 2022), a large multi-lingual dataset that provides morphological analyses of standalone words, organized into inflection tables in over 170 languages. Indeed, UniMorph was used in all of SIGMORPHON’s shared tasks in the last de- cade (Cotterell et al., 2016; Pimentel et al., 2021 inter alia). Such labeled morphological data rely heavily on the notion of a ‘word’, as words are the ele- ments occupying the cells of the inflection tables, and subsequently words are used as the input or output in the morphological tasks derived from these tables. However, a closer inspection of the data in UniMorph reveals that it is inherently in- consistent with respect to how words are defined. For instance, it is inconsistent with regards to the inclusion or exclusion of auxiliary verbs such as ‘‘will’’ and ‘‘be’’ as part of the inflection tables, and it is inconsistent in the features words in- flect for. A superficial attempt to fix this problem leads to the can of worms that is the theoreti- cal linguistic debate regarding the definition of the morpho-syntactic word, where it seems that a coherent cross-lingual definition of words is nowhere to be found …

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