Cited by 132Ethics and Social Impacts of AINatural Language Processing TechniquesTopic Modeling

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Societal Biases in Language Generation: Progress and Challenges

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Sheng, Emily, Chang, Kai-Wei, Natarajan, Prem, Peng, Nanyun·2021·Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

DOI 10.18653/v1/2021.acl-long.330

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Emily Sheng, Kai-Wei Chang, Prem Natarajan, Nanyun Peng. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

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“Furthermore, by ﬁltering out all words deemed “bad”, Bender et al. (2021) warns that we remove the discourse of marginalized populations.”
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501
Transformer-XL: Attentive language models beyond a fixed-length context
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Show all 99 references

Research works printed but not linked: 8

Saunders and Byrne (2020);
Saunders et al. (2020); Kocmi et al. (2020); Costa-jussà and de Jorge (2020); Costa-jussà et al. (2020);
Choubey et al. (2021); Saunders et al. (2021); Tomalin et al. (2021) Re-writing References
Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach. 2017. The problem with bias: Al- locative versus representational harms in machine learning. In 9th Annual Conference of the Special Interest Group for Computing, Information and So- ciety.
Kate Crawford. 2017. The trouble with bias. Keynote at NeurIPS.
Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hi- erarchical neural story generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 889-898.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
Alex Wang and Kyunghyun Cho. 2019. BERT has a mouth, and it must speak: BERT as a Markov random field language model. In Proceedings of the Workshop on Methods for Optimizing and Eval- uating Neural Language Generation, pages 30-36, Minneapolis, Minnesota. Association for Computa- tional Linguistics. · DOI

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Authors: Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N.
Affiliations: University of Southern California (US)
University of California, Los Angeles (US)
ORCID: Chang, Kai-Wei
Natarajan, Prem
Peng, Nanyun
Venue: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Publisher: Association for Computational Linguistics
Published: 2021
Pages: 4275-4293
DOI: 10.18653/v1/2021.acl-long.330
OpenAlex: W3174685870
Cited by: 131
References: 99
Citations by year: 2026: 18 · 2025: 28 · 2024: 18 · 2023: 43 · 2022: 16 · 2021: 8
FWCI: 19.25
Type: conference paper
Language: en
Open access: gold
doi.org
Licence: cc-by
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords: language generation, algorithmic fairness, algorithmic bias, natural language generation

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Societal Biases in Language Generation: Progress and Challenges

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