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.

XLTime使用预训练语言模型和多任务学习，在英语和非英语语言之间促进跨语言知识转移，以减轻目标语言数据短缺引起的问题，在法语、西班牙语、葡萄牙语和巴斯克语等语言上表现优于先前的自动SOTA方法，并且显著缩小了手工制作的HeidelTime方法的差距。

XLTime：一个用于时间表达式抽取的跨语言知识迁移框架