@inproceedings{guzman-nateras-etal-2022-shot,
title = "Few-Shot Cross-Lingual Learning for Event Detection",
author = "Guzman Nateras, Luis and
Lai, Viet and
Dernoncourt, Franck and
Nguyen, Thien",
editor = {Ataman, Duygu and
Gonen, Hila and
Ruder, Sebastian and
Firat, Orhan and
G{\"u}l Sahin, G{\"o}zde and
Mirzakhalov, Jamshidbek},
booktitle = "Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.mrl-1.2",
doi = "10.18653/v1/2022.mrl-1.2",
pages = "16--27",
abstract = "Cross-Lingual Event Detection (CLED) models are capable of performing the Event Detection (ED) task in multiple languages. Such models are trained using data from a source language and then evaluated on data from a distinct target language. Training is usually performed in the standard supervised setting with labeled data available in the source language. The Few-Shot Learning (FSL) paradigm is yet to be explored for CLED despite its inherent advantage of allowing models to better generalize to unseen event types. As such, in this work, we study the CLED task under an FSL setting. Our contribution is threefold: first, we introduce a novel FSL classification method based on Optimal Transport (OT); second, we present a novel regularization term to incorporate the global distance between the support and query sets; and third, we adapt our approach to the cross-lingual setting by exploiting the alignment between source and target data. Our experiments on three, syntactically-different, target languages show the applicability of our approach and its effectiveness at improving the cross-lingual performance of few-shot models for event detection.",
}
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<abstract>Cross-Lingual Event Detection (CLED) models are capable of performing the Event Detection (ED) task in multiple languages. Such models are trained using data from a source language and then evaluated on data from a distinct target language. Training is usually performed in the standard supervised setting with labeled data available in the source language. The Few-Shot Learning (FSL) paradigm is yet to be explored for CLED despite its inherent advantage of allowing models to better generalize to unseen event types. As such, in this work, we study the CLED task under an FSL setting. Our contribution is threefold: first, we introduce a novel FSL classification method based on Optimal Transport (OT); second, we present a novel regularization term to incorporate the global distance between the support and query sets; and third, we adapt our approach to the cross-lingual setting by exploiting the alignment between source and target data. Our experiments on three, syntactically-different, target languages show the applicability of our approach and its effectiveness at improving the cross-lingual performance of few-shot models for event detection.</abstract>
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%0 Conference Proceedings
%T Few-Shot Cross-Lingual Learning for Event Detection
%A Guzman Nateras, Luis
%A Lai, Viet
%A Dernoncourt, Franck
%A Nguyen, Thien
%Y Ataman, Duygu
%Y Gonen, Hila
%Y Ruder, Sebastian
%Y Firat, Orhan
%Y Gül Sahin, Gözde
%Y Mirzakhalov, Jamshidbek
%S Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F guzman-nateras-etal-2022-shot
%X Cross-Lingual Event Detection (CLED) models are capable of performing the Event Detection (ED) task in multiple languages. Such models are trained using data from a source language and then evaluated on data from a distinct target language. Training is usually performed in the standard supervised setting with labeled data available in the source language. The Few-Shot Learning (FSL) paradigm is yet to be explored for CLED despite its inherent advantage of allowing models to better generalize to unseen event types. As such, in this work, we study the CLED task under an FSL setting. Our contribution is threefold: first, we introduce a novel FSL classification method based on Optimal Transport (OT); second, we present a novel regularization term to incorporate the global distance between the support and query sets; and third, we adapt our approach to the cross-lingual setting by exploiting the alignment between source and target data. Our experiments on three, syntactically-different, target languages show the applicability of our approach and its effectiveness at improving the cross-lingual performance of few-shot models for event detection.
%R 10.18653/v1/2022.mrl-1.2
%U https://aclanthology.org/2022.mrl-1.2
%U https://doi.org/10.18653/v1/2022.mrl-1.2
%P 16-27
Markdown (Informal)
[Few-Shot Cross-Lingual Learning for Event Detection](https://aclanthology.org/2022.mrl-1.2) (Guzman Nateras et al., MRL 2022)
ACL
- Luis Guzman Nateras, Viet Lai, Franck Dernoncourt, and Thien Nguyen. 2022. Few-Shot Cross-Lingual Learning for Event Detection. In Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL), pages 16–27, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.