Multilingual Normalization of Temporal Expressions with Masked Language Models

The detection and normalization of temporal expressions is an important task and a preprocessing step for many applications. However, prior work on normalization is rule-based, which severely limits the applicability in real-world multilingual settings, due to the costly creation of new rules. We propose a novel neural method for normalizing temporal expressions based on masked language modeling. Our multilingual method outperforms prior rule-based systems in many languages, and in particular, for low-resource languages with performance improvements of up to 35 F1 on average compared to the state of the art.