Papers by Hamed Khanpour
Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Existing methods for debiasing are resource-intensive and costly. Existing solutions for debiansing require fine-tuning on downstream tasks. |
| Approach: | They propose to integrate Masked Language Modeling (MLM) training objectives into fine-tuning’s training process to debiase the PLMs. |
| Outcome: | The proposed approach outperforms the state-of-the-art baselines in terms of gender bias scores while improving PLMs’ performance solely using the downstream tasks’ dataset. |
Fine-Grained Emotion Detection in Health-Related Online Posts (D18-1)
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| Challenge: | Emotion detection from health-related posts is based on a health-specific vocabulary that people use in OHCs. |
| Approach: | They propose to use deep neural networks and lexicon-based features to detect emotions in health-related posts. |
| Outcome: | The proposed method uses high-level and abstract features derived from deep neural networks combined with lexicon-based features to detect emotions. |