Neural Morphological Tagging for Nguni Languages

TL;DR

This paper explores neural methods for morphological tagging in Nguni languages, demonstrating that neural taggers outperform rule-based systems and that training from scratch yields better results than fine-tuning pretrained models.

Contribution

It introduces neural morphological taggers for Nguni languages, compares different neural approaches, and evaluates their effectiveness against traditional rule-based parsers.

Findings

Neural taggers outperform rule-based baseline.

Training from scratch outperforms fine-tuning pretrained models.

Neural taggers are viable for Nguni morphological analysis.

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.