Cross-Domain Classification of Moral Values

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

2 Citations (Scopus)
12 Downloads (Pure)

Abstract

Moral values influence how we interpret and act upon the information we receive. Identifying human moral values is essential for artificially intelligent agents to co-exist with humans. Recent progress in natural language processing allows the identification of moral values in textual discourse. However, domain-specific moral rhetoric poses challenges for transferring knowledge from one domain to another. We provide the first extensive investigation on the effects of cross-domain classification of moral values from text. We compare a state-of-the-art deep learning model (BERT) in seven domains and four cross-domain settings. We show that a value classifier can generalize and transfer knowledge to novel domains, but it can introduce catastrophic forgetting. We also highlight the typical classification errors in cross-domain value classification and compare the model predictions to the annotators agreement. Our results provide insights to computer and social scientists that seek to identify moral rhetoric specific to a domain of discourse.

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationNAACL 2022 - Findings
PublisherAssociation for Computational Linguistics (ACL)
Pages2727-2745
Number of pages19
ISBN (Electronic)9781955917766
Publication statusPublished - 2022
Event2022 Findings of the Association for Computational Linguistics: NAACL 2022 - Seattle, United States
Duration: 10 Jul 202215 Jul 2022

Publication series

NameFindings of the Association for Computational Linguistics: NAACL 2022 - Findings

Conference

Conference2022 Findings of the Association for Computational Linguistics: NAACL 2022
Country/TerritoryUnited States
CitySeattle
Period10/07/2215/07/22

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