# XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction

arXiv: https://arxiv.org/abs/2205.01757 · HTML: https://mlsanity.com/paper/2205.01757/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Multi-Task Deep Neural Networks for Natural Language Understanding](https://mlsanity.com/paper/1901.11504/) (2019)
- [Understanding and Improving Information Transfer in Multi-Task Learning](https://mlsanity.com/paper/2005.00944/) (2020)
- [Entity Projection via Machine Translation for Cross-Lingual NER](https://mlsanity.com/paper/1909.05356/) (2019)
- [Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text](https://mlsanity.com/paper/2005.09392/) (2020)

## Built on by (most cited first, 4 of 4)

- [Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions](https://mlsanity.com/paper/2303.18103/) (2023)
- [TEI2GO: A Multilingual Approach for Fast Temporal Expression Identification](https://mlsanity.com/paper/2403.16804/) (2024)
- [Transformer-Based Temporal Information Extraction and Application: A Review](https://mlsanity.com/paper/2504.07470/) (2025)
- [ChunQiuTR: Time-Keyed Temporal Retrieval in Classical Chinese Annals](https://mlsanity.com/paper/2604.06997/) (2026)

## Abstract (arXiv)

Temporal Expression Extraction (TEE) is essential for understanding time in natural language. It has applications in Natural Language Processing (NLP) tasks such as question answering, information retrieval, and causal inference. To date, work in this area has mostly focused on English as there is a scarcity of labeled data for other languages. We propose XLTime, a novel framework for multilingual TEE. XLTime works on top of pre-trained language models and leverages multi-task learning to prompt cross-language knowledge transfer both from English and within the non-English languages. XLTime alleviates problems caused by a shortage of data in the target language. We apply XLTime with different language models and show that it outperforms the previous automatic SOTA methods on French, Spanish, Portuguese, and Basque, by large margins. XLTime also closes the gap considerably on the handcrafted HeidelTime method.