论文标题
切断前后之间的边缘：事件时间序列的神经体系结构
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
论文作者
论文摘要
在本文中，我们提出了一种神经架构和一套通过预测时间关系来订购事件的培训方法。我们提出的模型在文本的范围内接收一对事件，并确定它们之间的时间关系（之前，之后，相等，含糊）。鉴于此任务的关键挑战是缺乏注释的数据，我们的模型依赖于预验证的表示（即Roberta，Bert或Elmo），转移和多任务学习（通过利用补充数据集）以及自我培训技术。英语文档的Matres数据集上的实验建立了有关此任务的新最新技术。
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Before, After, Equal, Vague) between them. Given that a key challenge with this task is the scarcity of annotated data, our models rely on either pretrained representations (i.e. RoBERTa, BERT or ELMo), transfer and multi-task learning (by leveraging complementary datasets), and self-training techniques. Experiments on the MATRES dataset of English documents establish a new state-of-the-art on this task.
