Extracting temporal relations among events from unstructured text has extensive applications, such as temporal reasoning and question answering. While it is difficult, recent development of Neural-symbolic methods has shown promising results on solving similar tasks. Current temporal relation extraction methods usually suffer from limited expressivity and inconsistent relation inference. For example, in TimeML annotations, the concept of intersection is absent. Additionally, current methods do not guarantee the consistency among the predicted annotations. In this work, we propose SMARTER, a neural semantic parser, to extract temporal information in text effectively. SMARTER parses natural language to an executable logical form representation, based on a custom typed lambda calculus. In the training phase, dynamic programming on denotations (DPD) technique is used to provide weak supervision on logical forms. In the inference phase, SMARTER generates a temporal relation graph by executing the logical form. As a result, our neural semantic parser produces logical forms capturing the temporal information of text precisely. The accurate logical form representations of an event given the context ensure the correctness of the extracted relations.
翻译:从非结构化文本中提取事件之间的时间关系具有广泛的应用,例如时间推理和回答问题。虽然困难重重,但最近开发的神经-声波方法在解决类似任务方面已经显示出令人乐观的结果。当前时间关系提取方法通常具有有限的直观性和不一致的关系推论。例如,在时间ML说明中,没有交叉的概念。此外,目前的方法并不能保证预测说明的一致性。在这项工作中,我们提议神经语义分析师SMARTER(神经语义分析师)能够有效地从文本中提取时间信息。SMARTER(神经语言分析师)将自然语言转换为可执行的逻辑形式代表,其依据是定制型的羊羔积分法。在培训阶段,使用动态调分法(DPD)的编程用于对逻辑形式进行薄弱的监督。在引论阶段,SMARTER通过执行逻辑形式生成一个时间关系图。结果是,我们的神经语义语义分析师生成逻辑形式,以精确地捕捉到文本的时间信息。根据背景,精确的逻辑形式对事件进行表达。根据背景确保被提取的关系正确。