# Multilingual Normalization of Temporal Expressions with Masked Language Models

arXiv: https://arxiv.org/abs/2205.10399 · HTML: https://mlsanity.com/paper/2205.10399/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [PTR: Prompt Tuning with Rules for Text Classification](https://mlsanity.com/paper/2105.11259/) (2021)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [Template-free Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2109.13532/) (2021)
- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text](https://mlsanity.com/paper/2005.09392/) (2020)

## Built on by (most cited first, 7 of 7)

- [AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports](https://mlsanity.com/paper/2404.07765/) (2024)
- [Discourse-Aware In-Context Learning for Temporal Expression Normalization](https://mlsanity.com/paper/2404.07775/) (2024)
- [Transformer-Based Temporal Information Extraction and Application: A Review](https://mlsanity.com/paper/2504.07470/) (2025)
- [A Semantic Parsing Framework for End-to-End Time Normalization](https://mlsanity.com/paper/2507.06450/) (2025)
- [Reproducing Omitted Temporal Expressions in Japanese News for Retrieval-Augmented Applications](https://mlsanity.com/paper/2609.09569/) (2026)
- [Q-TIE: A Lightweight and Generalizable Re-ranking Framework for Temporal Information Retrieval](https://mlsanity.com/paper/2609.23880/) (2026)
- [TEMPS: Temporal Sentence Embeddings for Temporal Information Retrieval](https://mlsanity.com/paper/2609.28048/) (2026)

## Abstract (arXiv)

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.