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The UNESCO World Heritage List (WHL) includes the exceptionally valuablecultural and natural heritage to be preserved for mankind. Evaluating andjustifying the Outstanding Universal Value (OUV) is essential for each siteinscribed in the WHL, and yet a complex task, even for experts, since theselection criteria of OUV are not mutually exclusive. Furthermore, manualannotation of heritage values and attributes from multi-source textual data,which is currently dominant in heritage studies, is knowledge-demanding andtime-consuming, impeding systematic analysis of such authoritative documents interms of their implications on heritage management. This study appliesstate-of-the-art NLP models to build a classifier on a new dataset containingStatements of OUV, seeking an explainable and scalable automation tool tofacilitate the nomination, evaluation, research, and monitoring processes ofWorld Heritage sites. Label smoothing is innovatively adapted to improve themodel performance by adding prior inter-class relationship knowledge togenerate soft labels. The study shows that the best models fine-tuned from BERTand ULMFiT can reach 94.3% top-3 accuracy. A human study with expert evaluationon the model prediction shows that the models are sufficiently generalizable.The study is promising to be further developed and applied in heritage researchand practice.

WHOSe Heritage: Classification of UNESCO World Heritage "Outstanding Universal Value" Documents with Soft Labels