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- W4385569866 abstract "In this paper, we address the Event Detection task under a zero-shot cross-lingual setting where a model is trained on a source language but evaluated on a distinct target language for which there is no labeled data available. Most recent efforts in this field follow a direct transfer approach in which the model is trained using language-invariant features and then directly applied to the target language. However, we argue that these methods fail to take advantage of the benefits of the data transfer approach where a cross-lingual model is trained on target-language data and is able to learn task-specific information from syntactical features or word-label relations in the target language. As such, we propose a hybrid knowledge-transfer approach that leverages a teacher-student framework where the teacher and student networks are trained following the direct and data transfer approaches, respectively. Our method is complemented by a hierarchical training-sample selection scheme designed to address the issue of noisy labels being generated by the teacher model. Our model achieves state-of-the-art results on 9 morphologically-diverse target languages across 3 distinct datasets, highlighting the importance of exploiting the benefits of hybrid transfer." @default.
- W4385569866 created "2023-08-05" @default.
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- W4385569866 date "2023-01-01" @default.
- W4385569866 modified "2023-09-25" @default.
- W4385569866 title "Hybrid Knowledge Transfer for Improved Cross-Lingual Event Detection via Hierarchical Sample Selection" @default.
- W4385569866 doi "https://doi.org/10.18653/v1/2023.acl-long.296" @default.
- W4385569866 hasPublicationYear "2023" @default.
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