Tackling Language Modelling Bias in Support of Linguistic Diversity-Reference-Cited by-同舟云学术

Tackling Language Modelling Bias in Support of Linguistic Diversity

Published:2024-06-03 Issue: Volume:8 Page:562-572
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Container-title:The 2024 ACM Conference on Fairness, Accountability, and Transparency
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Author:

Bella Gábor¹^ORCID,Helm Paula²^ORCID,Koch Gertraud³^ORCID,Giunchiglia Fausto⁴^ORCID

Affiliation:

1. Lab-STICC, CNRS UMR 6285, IMT Atlantique, France

2. Faculty of Humanities, University of Amsterdam, Netherlands

3. Institute of Anthropological Studies on Culture and History, University of Hamburg, Germany

4. Department of Information Engineering and Computer Science, University of Trento, Italy

Funder

Data Science Centre, University of Amsterdam

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3630106.3658925

Reference57 articles.

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2. Khuyagbaatar Batsuren, Amarsanaa Ganbold, Altangerel Chagnaa, and Fausto Giunchiglia. 2019. Building the mongolian wordnet. In Proceedings of the 10th global WordNet conference. 238–244.

3. Khuyagbaatar Batsuren Ekaterina Vylomova Verna Dankers Tsetsuukhei Delgerbaatar Omri Uzan Yuval Pinter and Gábor Bella. 2024. Evaluating Subword Tokenization: Alien Subword Composition and OOV Generalization Challenge. arxiv:2404.13292 [cs.CL]

4. David Beer. 2017. The Social Power of Algorithms. Information, Communication & Society 20, 1 (2017).