Transitioning Representations between Languages for Cross-lingual Event Detection via Langevin Dynamics

Challenge:	Existing datasets for event detection (ED) are limited to a small set of popular languages due to the high cost of data annotation.
Approach:	They propose a method to develop cross-lingual transfer learning models in high-resource source languages . they aim to transition the representations for target-language examples into the source-language space .
Outcome:	Extensive experiments over three languages demonstrate the state-of-the-art in CLTL.

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Overview

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Challenge:	Existing methods for event detection (ED) rely on high-performance machine translation systems or manually aligned documents to achieve a decent performance.
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Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)

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Cross-lingual Structure Transfer for Zero-resource Event Extraction (2020.lrec-1)

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Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort (C18-1)

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GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding (2022.acl-long)

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Libo Qin, Qiguang Chen, Tianbao Xie, Qixin Li, Jian-Guang Lou, Wanxiang Che, Min-Yen Kan

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Event Extraction in Basque: Typologically Motivated Cross-Lingual Transfer-Learning Analysis (2024.lrec-main)

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Mikel Zubillaga, Oscar Sainz, Ainara Estarrona, Oier Lopez de Lacalle, Eneko Agirre

Multilinguality

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