论文标题
更深的任务特异性改善了联合实体和关系提取
Deeper Task-Specificity Improves Joint Entity and Relation Extraction
论文作者
论文摘要
多任务学习（MTL）是一种学习相关任务的有效方法，但是设计MTL模型需要确定哪些参数应该是特定于任务的，而不是任务之间的共享。我们研究了这个问题，即共同学习的命名实体识别（NER）和关系提取（RE）的问题，并提出了一种新型的神经结构，该神经结构比以前的工作更深入。特别是，我们为NER和RE任务介绍了其他特定于任务的双向RNN层，并分别针对不同数据集进行了共享和特定于任务的层数。我们为ADE数据集上的两个任务实现了最新的（SOTA）结果；在CONLL04数据集上，我们在NER任务和RE任务上实现了SOTA结果，同时使用可训练参数的数量级比当前的SOTA架构少。一项消融研究证实了其他特定任务层在实现这些结果方面的重要性。我们的工作表明，以前对联合NER和重新降低任务特异性的解决方案，并证明了正确平衡MTL方法的共享和特定于任务参数的数量的重要性。
Multi-task learning (MTL) is an effective method for learning related tasks, but designing MTL models necessitates deciding which and how many parameters should be task-specific, as opposed to shared between tasks. We investigate this issue for the problem of jointly learning named entity recognition (NER) and relation extraction (RE) and propose a novel neural architecture that allows for deeper task-specificity than does prior work. In particular, we introduce additional task-specific bidirectional RNN layers for both the NER and RE tasks and tune the number of shared and task-specific layers separately for different datasets. We achieve state-of-the-art (SOTA) results for both tasks on the ADE dataset; on the CoNLL04 dataset, we achieve SOTA results on the NER task and competitive results on the RE task while using an order of magnitude fewer trainable parameters than the current SOTA architecture. An ablation study confirms the importance of the additional task-specific layers for achieving these results. Our work suggests that previous solutions to joint NER and RE undervalue task-specificity and demonstrates the importance of correctly balancing the number of shared and task-specific parameters for MTL approaches in general.
