Zero-shot cross-lingual event argument role classification with enhanced dependency structure representation
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.
zero-shot cross-lingualevent argument role classificationdependency structureBiGRUinformation of dependency path
万方数据
维普
