Abstract
Accurately understanding temporal relations between events is a critical building block of diverse tasks, such as temporal reading comprehension (TRC) and relation extraction (TRE). For example in TRC, we need to understand the temporal semantic differences between the following two questions that are lexically near-identical: "What finished right before the decision?" or "What finished right after the decision?". To discern the two questions, existing solutions have relied on answer overlaps as a proxy label to contrast similar and dissimilar questions. However, we claim that answer overlap can lead to unreliable results, due to spurious overlaps of two dissimilar questions with coincidentally identical answers. To address the issue, we propose a novel approach that elicits proper reasoning behaviors through a module for predicting time spans of events. We introduce the Timeline Reasoning Network (TRN) operating in a two-step inductive reasoning process: In the first step model initially answers each question with semantic and syntactic information. The next step chains multiple questions on the same event to predict a timeline, which is then used to ground the answers. Results on the TORQUE and TB-dense, TRC and TRE tasks respectively, demonstrate that TRN outperforms previous methods by effectively resolving the spurious overlaps using the predicted timeline.
Abstract (translated)
准确理解事件之间的时间关系是多种任务的关键组成部分，例如时间阅读理解（TRC）和时间关系抽取（TRE）。例如，在TRC中，我们需要理解以下两个词面上几乎相同的问句之间的时间语义差异：“什么在决定之前结束？”或者“什么在决定之后结束？”为了区分这两个问题，现有的解决方案依赖于答案重叠作为类似和不相似问题的代理标签。然而，我们主张由于两个不相似的问题因巧合而具有相同答案导致的答案重叠可能会产生不可靠的结果。 为了解决这个问题，我们提出了一种新方法，通过预测事件的时间跨度模块来激发合理的推理行为。我们介绍了时间线推理网络（TRN），该网络在两步归纳推理过程中操作：第一步中，模型使用语义和句法信息初始回答每个问题；接下来的一步将多个关于同一事件的问题串联起来以预测时间线，然后利用这一时间线确定答案。TORQUE和TB-dense、TRC及TRE任务上的结果表明，TRN通过使用预测的时间线有效解决了虚假重叠问题，并且在性能上超越了先前的方法。
URL
https://arxiv.org/abs/2506.14213
