Detecting and normalizing temporal expressions is an essential step for many NLP tasks. While a variety of methods have been proposed for detection, best normalization approaches rely on hand-crafted rules. Furthermore, most of them have been designed only for English. In this paper we present a modular multilingual temporal processing system combining a fine-tuned Masked Language Model for detection, and a grammar-based normalizer. We experiment in Spanish and English and compare with HeidelTime, the state-of-the-art in multilingual temporal processing. We obtain best results in gold timex normalization, timex detection and type recognition, and competitive performance in the combined TempEval-3 relaxed value metric. A detailed error analysis shows that detecting only those timexes for which it is feasible to provide a normalization is highly beneficial in this last metric. This raises the question of which is the best strategy for timex processing, namely, leaving undetected those timexes for which is not easy to provide normalization rules or aiming for high coverage.

翻译：时间表达式的检测与归一化是许多自然语言处理任务的关键步骤。尽管已有多种检测方法被提出，但最优的归一化方法仍依赖于人工构建的规则，且这些方法大多仅针对英语设计。本文提出了一种模块化多语言时间处理系统，该系统结合了用于检测的微调掩码语言模型和基于语法的归一化器。我们在西班牙语和英语上进行实验，并与多语言时间处理领域的最新成果HeidelTime进行对比。在黄金标准时间表达式归一化、检测及类型识别任务上取得了最优结果，同时在TempEval-3宽松值综合评估指标上表现出竞争力。详细的错误分析表明，仅检测那些可提供归一化结果的时间表达式在该评估指标上具有显著优势。这一发现引发了关于时间表达式处理最优策略的思考：是选择忽略难以提供归一化规则的时间表达式，还是追求高覆盖率。