@inproceedings{guzman-nateras-etal-2023-hybrid,
    title = "Hybrid Knowledge Transfer for Improved Cross-Lingual Event Detection via Hierarchical Sample Selection",
    author = "Guzman Nateras, Luis  and
      Dernoncourt, Franck  and
      Nguyen, Thien",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.296",
    doi = "10.18653/v1/2023.acl-long.296",
    pages = "5414--5427",
    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.",
}

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.
Guzman Nateras, Luis Dernoncourt, Franck Nguyen, Thien
Hybrid Knowledge Transfer for Improved Cross-Lingual Event Detection via Hierarchical Sample Selection