事件论元角色分类是事件抽取中的子任务，旨在为事件中的候选论元分配相应的角色。事件语料标注规则复杂、人力耗费大，在很多语言中缺少相关标注文本。零样本跨语言事件论元角色分类可以利用源语言的丰富语料建立模型，然后直接应用于标注语料匮乏的目标语言端。围绕不同语言的事件文本在依存结构上的表达共性，提出了使用BiGRU网络模块对触发词到候选论元的依存路径信息进行额外编码的方法。本文设计的编码模块能灵活地与当前主流的基于深度学习框架的事件论元角色分类模型相联合。实验结果表明，本文提出的方法能更有效地完成跨语言迁移，提高多个基准模型的分类性能。

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.



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