  The UNESCO World Heritage List (WHL) includes the exceptionally valuable
cultural and natural heritage to be preserved for mankind. Evaluating and
justifying the Outstanding Universal Value (OUV) is essential for each site
inscribed in the WHL, and yet a complex task, even for experts, since the
selection criteria of OUV are not mutually exclusive. Furthermore, manual
annotation of heritage values and attributes from multi-source textual data,
which is currently dominant in heritage studies, is knowledge-demanding and
time-consuming, impeding systematic analysis of such authoritative documents in
terms of their implications on heritage management. This study applies
state-of-the-art NLP models to build a classifier on a new dataset containing
Statements of OUV, seeking an explainable and scalable automation tool to
facilitate the nomination, evaluation, research, and monitoring processes of
World Heritage sites. Label smoothing is innovatively adapted to improve the
model performance by adding prior inter-class relationship knowledge to
generate soft labels. The study shows that the best models fine-tuned from BERT
and ULMFiT can reach 94.3% top-3 accuracy. A human study with expert evaluation
on the model prediction shows that the models are sufficiently generalizable.
The study is promising to be further developed and applied in heritage research
and practice.
