OD - RTE : A One-Stage Object Detection Framework for Relational Triple Extraction (2023)

Abstract: The Relational Triple Extraction (RTE) task is a fundamental and essential information extraction task. Recently, the table-filling RTE methods have received lots of attention. Despite their success, they suffer from some inherent problems such as underutilizing regional information of triple. In this work, we treat the RTE task based on table-filling method as an Object Detection task and propose a one-stage Object Detection framework for Relational Triple Extraction (OD-RTE). In this framework, the vertices-based bounding box detection, coupled with auxiliary global relational triple region detection, ensuring that regional information of triple could be fully utilized. Besides, our proposed decoding scheme could extract all types of triples. In addition, the negative sampling strategy of relations in the training stage improves the training efficiency while alleviating the imbalance of positive and negative relations. The experimental results show that 1) OD-RTE achieves the state-of-the-art performance on two widely used datasets (i.e., NYT and WebNLG). 2) Compared with the best performing table-filling method, OD-RTE achieves faster training and inference speed with lower GPU memory usage. To facilitate future research in this area, the codes are publicly available at https://github.com/NingJinzhong/ODRTE .

## introduction the relational triple extraction (rte for short) aims to extract triples of the form (head, relation, tail) consisting of entity pairs and their relations from unstructured text, which is an important task of information extraction. in the early stage, traditional pipeline methods @xcite @xcite usually decompose the rte task into two independent steps of named entity recognition and relation extraction. table-filling method. pb denotes relation "place_of_birth" and pl denotes relation "place_lived". note that the table cells (representations of token pairs), the table, and the table regions occupied by the triples are aligned to the pixels, the image, and the objects on the visual side, respectively. ## methodology in this section, we first introduce the task definition. then we detail the implementation of od-rte, whose overall structure of od-rte is shown in figure 2 . ## conclusions in this work, we treat the rte task as an object detection task from a novel perspective and propose a one-stage relational triple extraction model od-rte. the vertices-based relational triple encoding method and bi-dw decoding algorithm used in od-rte enable it to handle various complex entity scenarios. in addition, the negative sampling strategy of relations in the training stage improves the training efficiency while alleviating the imbalance of positive and negative relations. compared with existing table-filling methods, our proposed od-rte can more effectively utilize the regional information for triple. experimental results on public datasets demonstrate that od-rte not only outperforms other state-of-the-art models in multiple complex scenarios, but also has high computational efficiency.