论文标题
遥远监督的隐性事件的时间推理
Temporal Reasoning on Implicit Events from Distant Supervision
论文作者
论文摘要
我们提出了Tracie，这是一种新颖的时间推理数据集，该数据集评估了系统理解隐式事件的程度 - 在自然语言文本中未明确提及的事件，但可以从中推断出来。这在时间推理研究中引入了一个新的挑战，该研究的先前工作集中在明确提到的事件上。人类读者可以通过常识推理推断隐式事件，从而对情况有更全面的理解，从而更好地理解时间。但是，我们发现，在预测隐式事件和明确事件之间的时间关系时，最新的模型在努力。为了解决这个问题，我们提出了一个神经符号的时间推理模型Symtime，该模型利用了大规模文本的遥远监督信号，并使用时间规则将起始时间和持续时间结合起来，以推断最终时间。 Symtime的表现优于Tracie上的强基线系统5％，而在零的先验知识训练设置中则比11％。我们的方法还推广到其他时间推理任务，这证明了Matres的1％-9％，这是一个明确的事件基准。
We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events -- events that are not mentioned explicitly in natural language text but can be inferred from it. This introduces a new challenge in temporal reasoning research, where prior work has focused on explicitly mentioned events. Human readers can infer implicit events via commonsense reasoning, resulting in a more comprehensive understanding of the situation and, consequently, better reasoning about time. We find, however, that state-of-the-art models struggle when predicting temporal relationships between implicit and explicit events. To address this, we propose a neuro-symbolic temporal reasoning model, SYMTIME, which exploits distant supervision signals from large-scale text and uses temporal rules to combine start times and durations to infer end times. SYMTIME outperforms strong baseline systems on TRACIE by 5%, and by 11% in a zero prior knowledge training setting. Our approach also generalizes to other temporal reasoning tasks, as evidenced by a gain of 1%-9% on MATRES, an explicit event benchmark.
