Papers by Boxin Shi

2 papers
Counterfactual Adversarial Learning with Representation Interpolation (2021.findings-emnlp)

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Challenge: Existing models with statistical bias are prone to memorized correlations . large pre-trained models such as BERT have revolutionized the model development paradigm in natural language processing .
Approach: They propose a framework to tackle the problem from a causal perspective using a latent space interpolation approach.
Outcome: Extensive experiments show that CAT achieves substantial performance improvement over SOTA across different downstream tasks, including sentence classification, natural language inference and question answering.
ReContraster: Making Your Posters Stand Out with Regional Contrast (2026.acl-long)

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Challenge: Effective poster design requires rapidly capturing attention and clearly conveying messages.
Approach: They propose a poster-based model that leverages regional contrast to make posters stand out.
Outcome: The proposed model outperforms state-of-the-art methods in producing striking posters.

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