论文标题
事件存在预测有助于触发跨语言的检测
Event Presence Prediction Helps Trigger Detection Across Languages
论文作者
论文摘要
事件检测和分类的任务对于大多数信息检索应用程序至关重要。我们表明，基于变压器的体系结构可以有效地将事件提取为序列标签任务。我们提出了句子级别和令牌级培训目标的组合，从而显着提高了基于BERT的事件提取模型的性能。我们的方法在ACE 2005数据的英语和中文数据上取得了新的最新性能。我们还测试了西班牙语的模型，比先前最佳性能模型的平均增益达到了2个绝对F1点。
The task of event detection and classification is central to most information retrieval applications. We show that a Transformer based architecture can effectively model event extraction as a sequence labeling task. We propose a combination of sentence level and token level training objectives that significantly boosts the performance of a BERT based event extraction model. Our approach achieves a new state-of-the-art performance on ACE 2005 data for English and Chinese. We also test our model on ERE Spanish, achieving an average gain of 2 absolute F1 points over prior best performing model.
