XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction (2205.01757v1)

Abstract: Temporal Expression Extraction (TEE) is essential for understanding time in natural language. It has applications in 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 LLMs 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 LLMs 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.