  Most data-to-text datasets are for English, so the difficulties of modelling
data-to-text for low-resource languages are largely unexplored. In this paper
we tackle data-to-text for isiXhosa, which is low-resource and agglutinative.
We introduce Triples-to-isiXhosa (T2X), a new dataset based on a subset of
WebNLG, which presents a new linguistic context that shifts modelling demands
to subword-driven techniques. We also develop an evaluation framework for T2X
that measures how accurately generated text describes the data. This enables
future users of T2X to go beyond surface-level metrics in evaluation. On the
modelling side we explore two classes of methods - dedicated data-to-text
models trained from scratch and pretrained language models (PLMs). We propose a
new dedicated architecture aimed at agglutinative data-to-text, the Subword
Segmental Pointer Generator (SSPG). It jointly learns to segment words and copy
entities, and outperforms existing dedicated models for 2 agglutinative
languages (isiXhosa and Finnish). We investigate pretrained solutions for T2X,
which reveals that standard PLMs come up short. Fine-tuning machine translation
models emerges as the best method overall. These findings underscore the
distinct challenge presented by T2X: neither well-established data-to-text
architectures nor customary pretrained methodologies prove optimal. We conclude
with a qualitative analysis of generation errors and an ablation study.
