WHOSe Heritage: Classification of UNESCO World Heritage Statements of "outstanding Universal Value" with Soft Labels

Nan Bai, Renqian Luo, Pirouz Nourian, Ana Pereira Roders

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)
53 Downloads (Pure)

Abstract

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 timeconsuming, 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 interclass 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.

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics, Findings of ACL
Subtitle of host publicationEMNLP 2021
EditorsMarie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-Tau Yih
Place of PublicationDominican Republic
PublisherAssociation for Computational Linguistics (ACL)
Pages366-384
Number of pages19
ISBN (Electronic)9781955917100
DOIs
Publication statusPublished - 2021
EventThe 2021 Conference on Empirical Methods in Natural Language Processing - Online and in the Barceló Bávaro Convention Centre, Punta Cana, Dominican Republic
Duration: 7 Nov 202111 Nov 2021
https://2021.emnlp.org

Publication series

NameFindings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021

Conference

ConferenceThe 2021 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2021
Country/TerritoryDominican Republic
CityPunta Cana
Period7/11/2111/11/21
Internet address

Keywords

  • Heritage values
  • Natural Language Processing
  • Outstanding Universal Value
  • Classification
  • Deep Learning
  • UNESCO World Heritage

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