Relation Classification (RC) is an important task in information extraction. In most real-world scenarios, the frequency of relations often follows a long-tailed and open-ended distribution. However, current efforts mainly focus on the partial frequency distribution of relations, which is limited in real-world applications. Meanwhile, prototypical network achieves remarkable performance among fields of deep supervised learning, few-shot learning and open set learning. Nevertheless, in the open world environment, it still suffers from the incompatible feature embedding problem as the novel and unknown relations come in. To address these problems, we propose an Open Generalized Prototypical Network with task-adaptive feature fusion for the open generalized few-shot relation classification. Extensive experiments are conducted on public large-scale datasets and our proposed model obtains the better performances.

中文翻译：

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开放世界环境中广义少样本关系分类的任务自适应特征融合


关系分类（RC）是信息抽取中的一项重要任务。在大多数现实场景中，关系的频率通常遵循长尾和开放式分布。然而，目前的工作主要集中在关系的部分频率分布上，这在实际应用中受到限制。同时，原型网络在深度监督学习、小样本学习和开放集学习领域取得了显着的性能。然而，在开放世界环境中，随着新颖和未知关系的出现，它仍然面临着不兼容的特征嵌入问题。为了解决这些问题，我们提出了一种具有任务自适应特征融合的开放广义原型网络，用于开放广义少数网络镜头关系分类。在公共大规模数据集上进行了大量的实验，我们提出的模型获得了更好的性能。