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Related Questions
- What are some common debiasing techniques used in word embeddings, such as word substitution, context-dependent embeddings, and regularization-based methods?
- How do these debiasing techniques improve model fairness by reducing biases in word embeddings?
- Can you provide examples of how debiasing techniques have been applied in real-world NLP tasks, such as sentiment analysis and text classification?
- What are some challenges and limitations of debiasing techniques, and how can they be addressed?
- How do debiasing techniques compare to other fairness techniques, such as data preprocessing and algorithmic fairness methods?
- What are some recent advancements in debiasing techniques for word embeddings, such as using adversarial training and transfer learning?
- How do debiasing techniques impact the performance of downstream NLP tasks, and what are some best practices for implementing debiasing techniques in NLP pipelines?
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