论文标题
建立实体关系提取的有效多任务互动：具有选择复发网络的统一框架
Towards Effective Multi-Task Interaction for Entity-Relation Extraction: A Unified Framework with Selection Recurrent Network
论文作者
论文摘要
实体关系提取旨在共同求解指定的实体识别（NER）和关系提取（RE）。最近的方法以管道方式或与共享编码器的双向隐式相互作用使用单向顺序信息传播。但是，由于NER和RE的不同任务形式之间的差距，他们仍然遭受了不良信息互动的困扰，这引发了一个有争议的问题，RE是否真的对NER有益。在此激励的情况下，我们提出了一个新颖而统一的级联框架，结合了顺序信息传播和隐式相互作用的优势。同时，它通过将实体关系提取作为统一的跨度拔除任务来重新定义实体关系提取，从而消除了这两个任务之间的差距。具体而言，我们提出了一个选择复发网络作为共享编码器，以编码特定于任务的独立和共享表示形式，并设计两个顺序信息传播策略，以实现NER和RE之间的顺序信息流。广泛的实验表明，我们的方法可以在两个常见的基准ACE05和SCIERC上实现最先进的结果，并有效地对多任务相互作用进行建模，从而实现了NER和RE的显着互补。
Entity-relation extraction aims to jointly solve named entity recognition (NER) and relation extraction (RE). Recent approaches use either one-way sequential information propagation in a pipeline manner or two-way implicit interaction with a shared encoder. However, they still suffer from poor information interaction due to the gap between the different task forms of NER and RE, raising a controversial question whether RE is really beneficial to NER. Motivated by this, we propose a novel and unified cascade framework that combines the advantages of both sequential information propagation and implicit interaction. Meanwhile, it eliminates the gap between the two tasks by reformulating entity-relation extraction as unified span-extraction tasks. Specifically, we propose a selection recurrent network as a shared encoder to encode task-specific independent and shared representations and design two sequential information propagation strategies to realize the sequential information flow between NER and RE. Extensive experiments demonstrate that our approaches can achieve state-of-the-art results on two common benchmarks, ACE05 and SciERC, and effectively model the multi-task interaction, which realizes significant mutual benefits of NER and RE.
