6 research outputs found
Morphological Inflection with Phonological Features
Recent years have brought great advances into solving morphological tasks,
mostly due to powerful neural models applied to various tasks as (re)inflection
and analysis. Yet, such morphological tasks cannot be considered solved,
especially when little training data is available or when generalizing to
previously unseen lemmas. This work explores effects on performance obtained
through various ways in which morphological models get access to subcharacter
phonological features that are the targets of morphological processes. We
design two methods to achieve this goal: one that leaves models as is but
manipulates the data to include features instead of characters, and another
that manipulates models to take phonological features into account when
building representations for phonemes. We elicit phonemic data from standard
graphemic data using language-specific grammars for languages with shallow
grapheme-to-phoneme mapping, and we experiment with two reinflection models
over eight languages. Our results show that our methods yield comparable
results to the grapheme-based baseline overall, with minor improvements in some
of the languages. All in all, we conclude that patterns in character
distributions are likely to allow models to infer the underlying phonological
characteristics, even when phonemes are not explicitly represented.Comment: ACL 2023 main conference; 8 pages, 1 figur
UniMorph 4.0:Universal Morphology
The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements made on several fronts over the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g. missing gender and macron information. We have also amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet
SIGMORPHON鈥揢niMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection
The 2022 SIGMORPHON鈥揢niMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe. We emphasize generalization along different dimensions this year by evaluating test items with unseen lemmas and unseen features separately under small and large training conditions. Across the five submitted systems and two baselines, the prediction of inflections with unseen features proved challenging, with average performance decreased substantially from last year. This was true even for languages for which the forms were in principle predictable, which suggests that further work is needed in designing systems that capture the various types of generalization required for the world鈥檚 languages