  The UNESCO World Heritage List (WHL) is to identify the exceptionally
valuable cultural and natural heritage to be preserved for mankind as a whole.
Evaluating and justifying the Outstanding Universal Value (OUV) of each
nomination in WHL is essentially important for a property to be inscribed, and
yet a complex task even for experts since the criteria are not mutually
exclusive. Furthermore, manual annotation of heritage values, which is
currently dominant in the field, 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 real-world dataset containing official
OUV justification statements, seeking an explainable, scalable, and less biased
automation tool to facilitate the nomination, evaluation, and monitoring
processes of World Heritage properties. Label smoothing is innovatively adapted
to transform the task smoothly between multi-class and multi-label
classification by adding prior inter-class relationship knowledge into the
labels, improving the performance of most baselines. The study shows that the
best models fine-tuned from BERT and ULMFiT can reach 94.3% top-3 accuracy,
which is promising to be further developed and applied in heritage research and
practice.
