面向事件时序与因果关系的联合识别方法

从事件时序关系与因果关系的关联性出发,提出基于神经网络的联合识别方法。将时序关系和因果关系识别分别作为主任务和辅助任务,设计共享辅助任务中编码层、解码层和编解码层的3种联合识别模型,通过主任务模型和辅助任务模型中的网络层进行信息共享,学习联合识别模型之间的特征信息。实验结果表明,联合识别方法能利用事件之间的因果信息有效提升时序关系的识别性能,且共享辅助任务中编解码层的联合识别模型更适用于事件时序关系与因果关系的联合识别。

Joint Identification Method for Temporal and Causal Relations of Events

Based on the correlation between temporal relations and causal relations of events,this paper proposes a joint identification method using neural network.The method takes the identification of temporal relations as the main task,and that of causal relations as auxiliary task.On this basis,three types of joint identification models of sharing encoding layer,decoding layer and encoding-decoding layer in auxiliary tasks are designed to enable information sharing through the network layer of the main task model and the auxiliary task model.Then feature information of joint identification models is learnt.Experimental results show that the joint identification method can use the causal information between events to significantly improve the identification performance of temporal relations.Also,the joint identification model of sharing encoding-decoding layer in auxiliary tasks is more suitable for the joint identification of temporal and causal relations of events.