# Neural Morphological Tagging for Nguni Languages

arXiv: https://arxiv.org/abs/2505.12949 · HTML: https://mlsanity.com/paper/2505.12949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 11 of 11)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [Canonical and Surface Morphological Segmentation for Nguni Languages](https://mlsanity.com/paper/2104.00767/) (2021)
- [Low-resource neural machine translation with morphological modeling](https://mlsanity.com/paper/2404.02392/) (2024)
- [Enhancing deep neural networks with morphological information](https://mlsanity.com/paper/2011.12432/) (2020)
- [A Truly Joint Neural Architecture for Segmentation and Parsing](https://mlsanity.com/paper/2402.02564/) (2024)
- [Morphological analysis using a sequence decoder](https://mlsanity.com/paper/1805.07946/) (2018)

## Abstract (arXiv)

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.