We present a study on leveraging multilingual pre-trained generative language
models for zero-shot cross-lingual event argument extraction (EAE). By
formulating EAE as a language generation task, our method effectively encodes
event structures and captures the dependencies between arguments. We design
language-agnostic templates to represent the event argument structures, which
are compatible with any language, hence facilitating the cross-lingual
transfer. Our proposed model finetunes multilingual pre-trained generative
language models to generate sentences that fill in the language-agnostic
template with arguments extracted from the input passage. The model is trained
on source languages and is then directly applied to target languages for event
argument extraction. Experiments demonstrate that the proposed model
outperforms the current state-of-the-art models on zero-shot cross-lingual EAE.
Comprehensive studies and error analyses are presented to better understand the
advantages and the current limitations of using generative language models for
zero-shot cross-lingual transfer EAE.

本文介绍利用多语言预训练生成式语言模型进行零 - shot 的跨语言事件论元提取的研究。通过将 EAE 视为语言生成任务，论文提出的方法能够有效编码事件结构并捕获论元之间的依赖关系，提出了语言无关的模板以表示事件论元结构，从而适用于任何语言，最终在源语言训练模型，并直接应用于目标语言以实现事件论元提取。实验表明，所提出的模型在零 - shot 跨语言 EAE 方面优于当前最先进的模型，论文对它的优势和现有限制进行了全面的研究和误差分析。