Papers by Misael Mongiovi’

1 papers
Towards Distribution-shift Robust Text Classification of Emotional Content (2023.findings-acl)

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Challenge: Recent work has shown that supervised models are more robust to change in domain and distribution, but the decrease in performance due to the distribution shift is still a major issue for supervised systems.
Approach: They propose to fine-tune supervised models on task-specific datasets to achieve out-of-distribution performance.
Outcome: The proposed model outperforms all available models in distribution and out of distribution with only a few thousand training samples.

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