Papers by Luis Guzman Nateras

1 papers
Hybrid Knowledge Transfer for Improved Cross-Lingual Event Detection via Hierarchical Sample Selection (2023.acl-long)

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Challenge: Recent efforts to train cross-lingual models on source language fail to take advantage of data transfer . current methods focus on learning task-specific information from syntactical features or word-label relations in target language.
Approach: They propose a hybrid knowledge-transfer approach that leverages a teacher-student framework . the model is evaluated on a distinct target language for which there is no labeled data .
Outcome: The proposed model achieves state-of-the-art results on 9 morphologically-diverse target languages across 3 distinct datasets.

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