Multilingual Normalization of Temporal Expressions with Masked Language Models

TL;DR

This paper introduces a neural, multilingual approach using masked language models to normalize temporal expressions, significantly outperforming rule-based systems especially in low-resource languages.

Contribution

The paper presents a novel neural normalization method leveraging masked language models, reducing reliance on rule-based systems for multilingual temporal expression normalization.

Findings

Outperforms prior rule-based systems in multiple languages

Achieves up to 33 F1 points improvement in low-resource languages

Demonstrates effectiveness across diverse multilingual settings

Abstract

The detection and normalization of temporal expressions is an important task and 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 33 F1 on average compared to the state of the art.