Temporal and numerical expression understanding is of great importance in many downstream Natural Language Processing (NLP) and Information Retrieval (IR) tasks. However, much previous work covers only a few sub-types and focuses only on entity extraction, which severely limits the usability of identified mentions. In order for such entities to be useful in downstream scenarios, coverage and granularity of sub-types are important; and, even more so, providing resolution into concrete values that can be manipulated. Furthermore, most previous work addresses only a handful of languages. Here we describe a multi-lingual evaluation dataset - NTX - covering diverse temporal and numerical expressions across 14 languages and covering extraction, normalization, and resolution. Along with the dataset we provide a robust rule-based system as a strong baseline for comparisons against other models to be evaluated in this dataset. Data and code are available at \url{https://aka.ms/NTX}.

翻译：时间与数值表达式的理解在许多下游自然语言处理和信息检索任务中至关重要。然而，以往的研究大多仅涵盖少数子类型且聚焦于实体抽取，严重限制了识别结果的可应用性。为使此类实体在下游场景中发挥作用，子类型的覆盖率与粒度至关重要，更重要的是需将其解析为可操作的具体值。此外，以往研究大多仅涉及少数语言。本文描述了一个多语言评估数据集——NTX，涵盖14种语言中多样化的时间与数值表达式，并覆盖抽取、归一化及解析任务。我们同时提供了一套稳健的基于规则的基线系统，作为该数据集上其他模型评估的强基准。数据和代码可从\url{https://aka.ms/NTX}获取。