Joint entity and relation extraction plays a pivotal role in various applications, notably in the construction of knowledge graphs. Despite recent progress, existing approaches often fall short in two key aspects: richness of representation and coherence in output structure. These models often rely on handcrafted heuristics for computing entity and relation representations, potentially leading to loss of crucial information. Furthermore, they disregard task and/or dataset-specific constraints, resulting in output structures that lack coherence. In our work, we introduce EnriCo, which mitigates these shortcomings. Firstly, to foster rich and expressive representation, our model leverage attention mechanisms that allow both entities and relations to dynamically determine the pertinent information required for accurate extraction. Secondly, we introduce a series of decoding algorithms designed to infer the highest scoring solutions while adhering to task and dataset-specific constraints, thus promoting structured and coherent outputs. Our model demonstrates competitive performance compared to baselines when evaluated on Joint IE datasets.

翻译：联合实体关系抽取在构建知识图谱等应用中发挥着关键作用。尽管近期取得进展，现有方法在表示丰富性与输出结构连贯性两方面仍存在不足：这些模型常依赖人工设计的启发式规则计算实体和关系表示，可能导致关键信息丢失；此外，它们忽略任务和/或数据集特定约束，导致输出结构缺乏连贯性。我们提出的EnriCo模型解决了上述缺陷：首先，为生成丰富且富有表现力的表示，模型利用注意力机制使实体和关系能动态确定精确抽取所需的相关信息；其次，我们设计了一系列解码算法，在严格遵守任务与数据集约束的前提下推理最高得分解，从而促进结构化连贯输出。在联合信息抽取数据集上的评估表明，相较基线模型，本模型展现出具有竞争力的性能。