论文标题
具有基于图的深膜注意模型的时间关系提取
Temporal Relation Extraction with a Graph-Based Deep Biaffine Attention Model
论文作者
论文摘要
时间信息提取在自然语言理解中起着至关重要的作用。以前的系统已经合并了高级神经语言模型，并成功提高了时间信息提取任务的准确性。但是，这些系统有两个主要的缺点。首先，他们无法利用预测中时间关系的双面性质。其次，它们在推理过程中涉及不可行的管道，这会带来很少的性能增益。为此，我们提出了一个基于深度Biaffine注意的新型时间信息提取模型，以有效，准确地提取事件之间的时间关系。我们的模型之所以具有性能，是因为我们直接执行关系提取任务，而不是将事件注释作为关系提取的先决条件。此外，我们的体系结构使用多层感知器（MLP）和Biaffine的关注来分别预测ARC和关系标签，从而通过利用时间关系的双面性质来提高关系检测准确性。我们通过实验表明，我们的模型在时间关系提取中实现了最新的表现。
Temporal information extraction plays a critical role in natural language understanding. Previous systems have incorporated advanced neural language models and have successfully enhanced the accuracy of temporal information extraction tasks. However, these systems have two major shortcomings. First, they fail to make use of the two-sided nature of temporal relations in prediction. Second, they involve non-parallelizable pipelines in inference process that bring little performance gain. To this end, we propose a novel temporal information extraction model based on deep biaffine attention to extract temporal relationships between events in unstructured text efficiently and accurately. Our model is performant because we perform relation extraction tasks directly instead of considering event annotation as a prerequisite of relation extraction. Moreover, our architecture uses Multilayer Perceptrons (MLP) with biaffine attention to predict arcs and relation labels separately, improving relation detecting accuracy by exploiting the two-sided nature of temporal relationships. We experimentally demonstrate that our model achieves state-of-the-art performance in temporal relation extraction.
