Defining and Evaluating Fair Natural Language Generation
By C. Yeo, Alyssa Chen. 2020
26 citations recorded by the source corpus.
Our work focuses on the biases that emerge in the natural language generation (NLG) task of sentence completion. In this paper, we introduce a mathematical framework of fairness for NLG followed by an evaluation of gender biases in two state-of-the-art language models. Our analysis provides a theoretical formulation for biases in NLG and empirical evidence that existing language generation models embed gender bias.
Subfields: Artificial Intelligence, Natural Language Generation, AI Ethics, Fairness & Society
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Goran Glavas
- Ivan Vulic
- Anne Lauscher
- Soumya Barikeri
- Mykola Pechenizkiy
- Zijing Shi
- Meng Fang
- Ling Chen
In AI Ethics, Fairness & Society
- Anne Lauscher
- Goran Glavas
- Mykola Pechenizkiy
- Ivan Vulic
- Soumya Barikeri
- Sunipa Dev
- Indrė Žliobaitė
- Faisal Kamiran
Citations in the corpus
- RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models (2021, background): “, 2020a,b), albeit some more research has recently emerged in the wider context of biases in generalpurpose language generation models (Qian et al., 2019; Sheng et al., 2019; Nadeem et al., 2020; Yeo and Chen, 2020).”
- CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models (2023, background): “A growing amount of work (Qian et al., 2019; Yeo and Chen, 2020; Nadeem et al., 2021) has started to study biases in language generation models.”
- Theories of “Gender” in NLP Bias Research (2022, unknown): “10787 [162] Catherine Yeo and Alyssa Chen.”