Abstract: Event argument role classification is a subtask in event extraction, which aims to assign corresponding roles to candidate arguments in the event. Event corpus labeling rules are complicated and labor-intensive, and there is a lack of relevant labeling texts in many languages. Zero-shot cross-lingual event argument role classification can use source-side corpus with rich annotations to build a model, and then apply it directly to a target-side counterpart task where the labeled corpus is scarce. Focusing on the commonalities of the dependency structure of event texts between different languages, this paper further proposes a method that uses the BiGRU network to encode dependency paths connecting trigger words to candidate arguments. The proposed encoder can be flexibly integrated into several mainstream models in a deep learning framework for event argument role classification. The experimental results demonstrate that the proposed method is more effective in completing cross-lingual migration and improving the classification performance of multiple baselines.

Key words: zero-shot cross-lingual, event argument role classification, dependency structure, BiGRU, information of dependency path ,