  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.
