Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
Comput Intell Neurosci. 2022 Apr 28;2022:5680971. doi: 10.1155/2022/5680971. eCollection 2022.
Determining the temporal relationship between events has always been a challenging natural language understanding task. Previous research mainly relies on neural networks to learn effective features or artificial language features to extract temporal relationships, which usually fails when the context between two events is complex or extensive. In this paper, we propose our JSSA (Joint Semantic and Syntactic Attention) model, a method that combines both coarse-grained information from semantic level and fine-grained information from syntactic level. We utilize neighbor triples of events on syntactic dependency trees and events triple to construct syntactic attention served as clue information and prior guidance for analyzing the context information. The experiment results on TB-Dense and MATRES datasets have proved the effectiveness of our ideas.
确定事件之间的时间关系一直是一项具有挑战性的自然语言理解任务。以前的研究主要依赖于神经网络来学习有效的特征或人工语言特征来提取时间关系，但当两个事件之间的上下文复杂或广泛时，这种方法通常会失败。在本文中，我们提出了我们的 JSSA（联合语义和句法注意力）模型，这是一种结合语义层次的粗粒度信息和句法层次的细粒度信息的方法。我们利用句法依存树上的事件三元组和事件三元组构建句法注意力，作为分析上下文信息的线索信息和先验指导。在 TB-Dense 和 MATRES 数据集上的实验结果证明了我们的想法的有效性。
