Papers by Shengyu Jia
Mitigating Gender Bias Amplification in Distribution by Posterior Regularization (2020.acl-main)
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| Challenge: | Recent studies show that data-driven machine learning models carry societal biases in the dataset they trained on. |
| Approach: | They propose to calibrate top predictions of a model by injecting corpus-level constraints to ensure that the gender disparity is not amplified. |
| Outcome: | The proposed method can almost remove bias amplification in the distribution with little loss of performance. |
Neural Gibbs Sampling for Joint Event Argument Extraction (2020.aacl-main)
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| Challenge: | Existing methods for event argument extraction cannot adequately model the correlation between event arguments and their roles. |
| Approach: | They propose a Bayesian model to jointly extract event arguments using Gibbs sampling . they train two neural networks to model prior distribution and conditional distribution over event arguments . |
| Outcome: | The proposed model can achieve comparable results to existing methods on two widely-used datasets. |