Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions

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, the coverage and granularity of sub-types are important; and even more so, the resolution into concrete values that can be manipulated. Moreover, most previous work addresses only a handful of languages. Here we propose both a multi-lingual evaluation dataset – NTX – covering diverse temporal and numerical expressions across 14 languages; including 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 can be accessed at https://aka.ms/NTX.