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Values That Are Explicitly Present in Fairy Tales: Comparing Samples from German, Italian and Portuguese Traditions

arXiv 2402.08318 ↗First posted 13 Feb 2024cs.CL cs.CYcited by 0

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Abstract (arXiv)

Looking at how social values are represented in fairy tales can give insights about the variations in communication of values across cultures. We study how values are communicated in fairy tales from Portugal, Italy and Germany using a technique called word embedding with a compass to quantify vocabulary differences and commonalities. We study how these three national traditions differ in their explicit references to values. To do this, we specify a list of value-charged tokens, consider their word stems and analyse the distance between these in a bespoke pre-trained Word2Vec model. We triangulate and critically discuss the validity of the resulting hypotheses emerging from this quantitative model. Our claim is that this is a reusable and reproducible method for the study of the values explicitly referenced in historical corpora. Finally, our preliminary findings hint at a shared cultural understanding and the expression of values such as Benevolence, Conformity, and Universalism across the studied cultures, suggesting the potential existence of a pan-European cultural memory.

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References · 12

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  3. 2021Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpuscited by 481
  4. 2020Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT modelcited by 87
  5. 2021Mitigating Language-Dependent Ethnic Bias in BERTcited by 87
  6. 2021Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Modelscited by 57
  7. 2019A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domainscited by 31
  8. 2019Training Temporal Word Embeddings with a Compasscited by 22
  9. 2023A Survey on Contextualised Semantic Shift Detectioncited by 21
  10. 2021A comparative evaluation and analysis of three generations of Distributional Semantic Modelscited by 19
  11. 2023Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children's Fairy Talescited by 3
  12. 2024How BERT Speaks Shakespearean English? Evaluating Historical Bias in Contextual Language Modelscited by 2

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