Event Temporal Relation Extraction with Attention Mechanism and Graph Neural Network

• 作者: Xiaoliang Xu Tong Gao Yuxiang Wang Xinle Xuan
• 来源期刊: 清华大学学报自然科学版（英文版）
• 年，卷（期）: 2022,（1）
• 所属期刊栏目: SPECIAL SECTION ON CLOUD COMPUTING AND BIG DADA
• 页码范围: 79-90
• 页数: 12页
• 语种: 英文
• DOI: 10.26599/TST.2020.9010063

摘要

Event temporal relation extraction is an important part of natural language processing.Many models are being used in this task with the development of deep learning.However,most of the existing methods cannot accurately obtain the degree of association between different tokens and events,and event-related information cannot be effectively integrated.In this paper,we propose an event information integration model that integrates event information through multilayer bidirectional long short-term memory (Bi-LSTM) and attention mechanism.Although the above scheme can improve the extraction performance,it can still be further optimized.To further improve the performance of the previous scheme,we propose a novel relational graph attention network that incorporates edge attributes.In this approach,we first build a semantic dependency graph through dependency parsing,model a semantic graph that considers the edges' attributes by using top-k attention mechanisms to learn hidden semantic contextual representations,and finally predict event temporal relations.We evaluate proposed models on the TimeBank-Dense dataset.Compared to previous baselines,the Micro-F1 scores obtained by our models improve by 3.9％ and 14.5％,respectively.