Despite achieving state-of-the-art accuracy on temporal ordering of events, neural models showcase significant gaps in performance. Our work seeks to fill one of these gaps by leveraging an under-explored dimension of textual semantics: rich semantic information provided by explicit textual time cues. We develop STAGE, a system that consists of a novel temporal framework and a parser that can automatically extract time cues and convert them into representations suitable for integration with neural models. We demonstrate the utility of extracted cues by integrating them with an event ordering model using a joint BiLSTM and ILP constraint architecture. We outline the functionality of the 3-part STAGE processing approach, and show two methods of integrating its representations with the BiLSTM-ILP model: (i) incorporating semantic cues as additional features, and (ii) generating new constraints from semantic cues to be enforced in the ILP. We demonstrate promising results on two event ordering datasets, and highlight important issues in semantic cue representation and integration for future research.

翻译：尽管在时间顺序上实现了最新准确性,但神经模型展示了显著的绩效差距。我们的工作力求通过利用文字语义学探索不足的层面来填补其中一个差距:由明确的文字时间提示提供的丰富的语义信息。我们开发了STAGE系统,该系统由新的时间框架和剖析器组成,可以自动提取时间提示,将其转化为适合与神经模型整合的表述。我们通过使用BILSTM和ILP联合制约结构,将抽取的提示与事件排序模型相结合,展示了这些提示的效用。我们概述了STAGE三部分处理方法的功能,并展示了将其表述与BILSTM-ILP模型整合的两种方法:(一) 将语义提示作为附加特征,以及(二) 从将在ILP中执行的语义提示产生新的制约。我们展示了两个事件订购数据集的有希望的结果,并突出了语义提示表达和整合的重要问题,供未来研究。