Tagging based relational triple extraction methods are attracting growing research attention recently. However, most of these methods take a unidirectional extraction framework that first extracts all subjects and then extracts objects and relations simultaneously based on the subjects extracted. This framework has an obvious deficiency that it is too sensitive to the extraction results of subjects. To overcome this deficiency, we propose a bidirectional extraction framework based method that extracts triples based on the entity pairs extracted from two complementary directions. Concretely, we first extract all possible subject-object pairs from two paralleled directions. These two extraction directions are connected by a shared encoder component, thus the extraction features from one direction can flow to another direction and vice versa. By this way, the extractions of two directions can boost and complement each other. Next, we assign all possible relations for each entity pair by a biaffine model. During training, we observe that the share structure will lead to a convergence rate inconsistency issue which is harmful to performance. So we propose a share-aware learning mechanism to address it. We evaluate the proposed model on multiple benchmark datasets. Extensive experimental results show that the proposed model is very effective and it achieves state-of-the-art results on all of these datasets. Moreover, experiments show that both the proposed bidirectional extraction framework and the share-aware learning mechanism have good adaptability and can be used to improve the performance of other tagging based methods. The source code of our work is available at: https://github.com/neukg/BiRTE.
翻译:然而,这些方法大多采用单向提取框架,首先提取所有主题,然后根据所提取的主题同时提取对象和关系。这个框架有一个明显的缺陷,即它太敏感到对主题的提取结果太敏感。为了克服这一缺陷,我们建议采用双向提取框架方法,根据从两个互补方向提取的实体对子,从两个互补方向提取三重。具体地说,我们首先从两个平行方向提取所有可能的主题-目标对子。这两个提取方向由一个共享的编码器组件连接,因此从一个方向提取的特征可以流向另一个方向,反之亦然。这样,从两个方向提取的特征可以促进和补充另一个主题的提取结果。接下来,我们用双向模型为每个实体配对分配所有可能的关系。在培训期间,我们观察到,股份结构将导致一个混合率不一致的问题,对业绩有害。因此我们建议一个共享/认知的学习机制来解决这个问题。我们用多个基准数据集来评估拟议模型,因此,从一个方向的提取特征可以流向另一个方向,反向另一个方向的提取特征。广泛的实验结果显示,两个方向的提取过程的提取结果可以相互促进和补充。我们提议的模型的模型的模型使用。