Year: 2025
Type: conference-paper
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.
Authors: Cael Marquard, Simbarashe Mawere, Francois Meyer
Institution: University of Cape Town
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412889330