Bias Neutralization in Non-Parallel Texts: A Cyclic Approach with Auxiliary Guidance (2023.emnlp-main)
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| Challenge: | Existing methods for subjective bias neutralization rely on parallel text training and lose important bias-independent context. |
| Approach: | They propose a bias neutralization model that uses an auxiliary guided cycle consistent GAN to train with a combination of adversarial, cycleconsistency and identity mapping loss. |
| Outcome: | The proposed model significantly improves subjective bias neutralization compared to existing methods. |
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| Challenge: | a new task is proposed to reduce media news framing bias by generating a neutral summary from multiple news articles of the varying political leanings. |
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WIKIBIAS: Detecting Multi-Span Subjective Biases in Language (2021.findings-emnlp)
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| Challenge: | a particular type of bias is subjective bias, which introduces improper attitudes or presents a statement with the presupposition of truth. |
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Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training (N19-1)
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| Challenge: | Recent work has shown superior performance for non-adversarial methods in more challenging language pairs. |
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Balancing out Bias: Achieving Fairness Through Balanced Training (2022.emnlp-main)
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| Challenge: | Existing approaches to reducing group bias do not account for correlations between author demographics and linguistic variables, limiting their effectiveness. |
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| Challenge: | Existing methods for debiasing large language models incur high human and computational costs and are limited in their effectiveness. |
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Bias and Fairness in Natural Language Processing (D19-2)
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| Challenge: | a tutorial will review the history of bias and fairness studies in machine learning and language processing . |
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Improving Bias Mitigation through Bias Experts in Natural Language Understanding (2023.emnlp-main)
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| Challenge: | Existing approaches to mitigate the detrimental effect of bias on the network include debiasing methods that down-weight the biased examples identified by an auxiliary model, which is trained with explicit bias labels. |
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NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives (2021.emnlp-main)
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| Challenge: | Recent years have seen an increasing need for gender-neutral and inclusive language. |
| Approach: | They propose a rule-based and a neural approach to gender-neutral rewriting for English . they use manually curated synthetic and natural data to train a rewriter . |
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It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons (2024.eacl-long)
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| Challenge: | a new model to score subjective bias in documents is developed to perform pairwise comparisons . a recent study shows that the model can be explained and validated for other domains based on the training data. |
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