Papers with Predictive models
Learning Fair Representations via Rate-Distortion Maximization (2022.tacl-1)
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| Challenge: | Empirical evaluations show that FaRM debiases representations with or without a target task at hand. |
| Approach: | They propose a method that makes representations belonging to the same protected attribute class uncorrelated, using the rate-distortion function. |
| Outcome: | Empirical results show that the proposed technique achieves state-of-the-art performance on several datasets and leaks significantly less protected attribute information against an attack by a non-linear probing network. |
Explaining Speech Classification Models via Word-Level Audio Segments and Paralinguistic Features (2024.eacl-long)
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| Challenge: | Existing explanations for speech classification models are difficult to interpret and make mistakes. |
| Approach: | They propose to explain speech classification models by using word-level and paralinguistic attributes to measure the impact of each audio segment aligned with a word on the outcome. |
| Outcome: | The proposed explanations correctly represent the model’s inner workings and are plausible to humans. |
Residualized Factor Adaptation for Community Social Media Prediction Tasks (D18-1)
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| Challenge: | Existing approaches to social media language capture only socio-demographic contexts, such as age, education rates, race, and gender. |
| Approach: | They propose a method which integrates community attributes and adapts linguistic features to community attributes. |
| Outcome: | The proposed model integrates community attributes and adapts linguistic features to community attributes. |