The idea of using multi-task learning approaches to address the joint
extraction of entity and relation is motivated by the relatedness between the
entity recognition task and the relation classification task. Existing methods
using multi-task learning techniques to address the problem learn interactions
among the two tasks through a shared network, where the shared information is
passed into the task-specific networks for prediction. However, such an
approach hinders the model from learning explicit interactions between the two
tasks to improve the performance on the individual tasks. As a solution, we
design a multi-task learning model which we refer to as recurrent interaction
network which allows the learning of interactions dynamically, to effectively
model task-specific features for classification. Empirical studies on two
real-world datasets confirm the superiority of the proposed model.

采用多任务学习方法解决实体和关系联合提取问题，提出了一种称为循环交互网络的多任务学习模型，能够动态地学习交互来有效地模拟分类的任务特征，并在两个真实数据集上进行实证研究验证了该模型的优越性。