基于联合模型的端到端事件可信度识别

End-to-End Event Factuality Identification with Joint Model

事件可信度是对文本中事件真实情况的一种描述,是自然语言处理领域许多相关应用的基本任务。目前,大多数关于事件可信度的相关研究都是使用标注的事件进行事件可信度识别,不方便实际应用,并且忽略了不同事件源对事件可信度的影响。针对现有问题,提出了一个端到端的事件可信度识别的联合模型JESF。该模型可以同时进行事件识别、事件源识别、事件可信度识别3个任务;使用BERT(Bidirectional Encoder Representations from Transformers)和语言学特征加强单词的语义表示;使用注意力机制(Attention)和依存句法树构建图卷积神经网络(Graph Convolutional Network,GCN),以有效地提取语义和句法特征。特别地,该模型也可以应用于只考虑默认源(文本作者)的事件可信度任务。在FactBank,Meantime,UW,UDS-IH2等语料上的实验结果显示,所提模型优于基准模型。

Event factuality is the description of the real situation of events in text.It is the basic task of many related applications in the field of natural language processing.At present,most researches on event factuality use labeled events to identify event factuality,which is inconvenient for practical application,and ignores the impact of different event sources on event factuality.Aiming at the existing problems,an end-to-end joint model JESF for event factuality identification is proposed.The model can carry out three tasks at the same time:event identification,event source identification and event factuality identification.Using bidirectional encoder representations from transformers(BERT)and linguistic features to strengthen the semantic representation of words.Graph convolutional network(GCN)is constructed by using attention mechanism and dependency syntax tree to effectively extract semantic and syntactic features.In particular,the model can also be applied to the task of event factuality considering only the default source(text author).Experimental results on FactBank,Meantime,UW and UDS-IH2 show that the proposed model is better than the benchmark model.