Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
This work addresses a specific problem in natural language processing for researchers and practitioners, but it is incremental as it builds on existing multi-task learning approaches.
The paper tackled the joint extraction of entities and relations by proposing a recurrent interaction network that learns explicit interactions between tasks, achieving superior performance on two real-world datasets.
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.