Papers by Eduard Poesina

2 papers
Text Classification Under Class Distribution Shift: A Survey (2026.eacl-long)

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Challenge: ML models assume that training and test data are sampled from the same distribution, but in daily practice, this assumption is often broken.
Approach: They survey articles studying open-set text classification to understand the distribution shifts and mitigation approaches for each problem setup.
Outcome: The proposed methods can solve problems caused by the shifting class distribution in open-set text classification and related tasks.
A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus (2024.acl-long)

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Challenge: Natural language inference (NLI) is an actively studied topic serving as a proxy for natural language understanding.
Approach: They propose to use a Romanian NLI corpus to analyze sentence pairs . they use multiple machine learning methods to establish competitive baselines .
Outcome: The proposed model improves on the best model by employing a new curriculum learning strategy based on data cartography.

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