An Adaptive Hybrid Prototypical Network for Interactive Few-Shot Relation Extraction

Few-shot relation extraction constitutes a critical task in natural language processing. Its aim is to train a model using a limited number of labeled samples when labeled data are scarce, thereby enabling the model to rapidly learn and accurately identify relationships between entities within textual data. Prototypical networks are extensively utilized for simplicity and efficiency in few-shot relation extraction scenarios. Nevertheless, the prototypical networks derive their prototypes by averaging the feature instances within a given category. In cases where the instance size is limited, the prototype may not represent the true category centroid adequately, consequently diminishing the accuracy of classification. In this paper, we propose an innovative approach for few-shot relation extraction, leveraging instances from the query set to enhance the construction of prototypical networks based on the support set. Then, the weights are dynamically assigned by quantifying the semantic similarity between sentences. It can strengthen the emphasis on critical samples while preventing potential bias in class prototypes, which are computed using the mean value within prototype networks under small-size scenarios. Furthermore, an adaptive fusion module is introduced to integrate prototype and relational information more deeply, resulting in more accurate prototype representations. Extensive experiments have been performed on the widely used FewRel benchmark dataset. The experimental findings demonstrate that our AIRE model surpasses the existing baseline models, especially the accuracy, which can reach 91.53% and 86.36% on the 5-way 1-shot and 10-way 1-shot tasks, respectively.