Temporal validity is an important property of text that is useful for many downstream applications, such as recommender systems, conversational AI, or story understanding. Existing benchmarking tasks often require models to identify the temporal validity duration of a single statement. However, in many cases, additional contextual information, such as sentences in a story or posts on a social media profile, can be collected from the available text stream. This contextual information may greatly alter the duration for which a statement is expected to be valid. We propose Temporal Validity Change Prediction, a natural language processing task benchmarking the capability of machine learning models to detect contextual statements that induce such change. We create a dataset consisting of temporal target statements sourced from Twitter and crowdsource sample context statements. We then benchmark a set of transformer-based language models on our dataset. Finally, we experiment with temporal validity duration prediction as an auxiliary task to improve the performance of the state-of-the-art model.

翻译：时间有效性是文本的一个重要属性，对于推荐系统、对话式人工智能或故事理解等众多下游应用具有实用价值。现有基准测试任务通常要求模型识别单一陈述的时间有效持续时间。然而，在许多情况下，可以从可用文本流中收集额外的上下文信息，例如故事中的句子或社交媒体帖子。这些上下文信息可能显著改变预期中陈述有效的持续时间。我们提出时间有效性变化预测这一自然语言处理任务，旨在基准测试机器学习模型检测引发此类变化的上下文语句的能力。我们构建了一个数据集，其中包含从推特收集的时间目标陈述以及众包采集的样本上下文语句。随后，我们在数据集上对一系列基于变换器的语言模型进行基准评估。最后，我们尝试将时间有效持续时间预测作为辅助任务，以提升最先进模型的性能。