Marquard, Cael and Mawere, Simbarashe and Meyer, Francois (2025) Neural Morphological Tagging for Nguni Languages, Proceedings of Sixth Workshop on African Natural Language Processing (AfricaNLP 2025), 31 July 2025, Vienna, Austria, 210-220, Association for Computational Linguistics.
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Abstract
Morphological parsing is the task of decomposing words into morphemes, the smallest units of meaning in a language, and labelling their grammatical roles. It is a particularly challenging task for agglutinative languages, such as the Nguni languages of South Africa, which construct words by concatenating multiple morphemes. A morphological parsing system can be framed as a pipeline with two separate components, a segmenter followed by a tagger. This paper investigates the use of neural methods to build morphological taggers for the four Nguni languages. We compare two classes of approaches: training neural sequence labellers (LSTMs and neural CRFs) from scratch and finetuning pretrained language models. We compare performance across these two categories, as well as to a traditional rule-based morphological parser. Neural taggers comfortably outperform the rule-based baseline and models trained from scratch tend to outperform pretrained models. We also compare parsing results across different upstream segmenters and with varying linguistic input features. Our findings confirm the viability of employing neural taggers based on pre-existing morphological segmenters for the Nguni languages.
| Item Type: | Conference paper |
|---|---|
| Uncontrolled Keywords: | natural language processing,nguni,morphological parsing,isixhosa,isizulu,isindebele,siswati,morphology |
| Subjects: | Computing methodologies > Artificial intelligence > Natural language processing Computing methodologies > Artificial intelligence > Natural language processing > Phonology / morphology |
| Alternate Locations: | https://aclanthology.org/2025.africanlp-1.31/ |
| Date Deposited: | 13 Oct 2025 12:24 |
| Last Modified: | 13 Oct 2025 12:24 |
| URI: | https://pubs.cs.uct.ac.za/id/eprint/1740 |
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