XLTU: A Cross-Lingual Model in Temporal Expression Extraction for Uyghur

Yifei Liang, Lanying Li, Rui Liu, Ahtam Ahmat, Lei Jiang

Abstract: Temporal expression extraction (TEE) plays a crucial role in natural language processing (NLP) tasks, enabling the capture of temporal information for downstream tasks such as logical reasoning and information retrieval. However, current TEE research mainly focuses on resource-rich languages like English, leaving a gap for minor languages (e.g. Uyghur) in research. To address these issues, we create an English-Uyghur cross-lingual dataset specifically for the task of temporal expression extraction in Uyghur. Besides, considering the unique characteristics of Uyghur, we propose XLTU, a Cross-Lingual model in Temporal expression extraction for Uyghur, and utilize multi-task learning to help transfer the knowledge from English to Uyghur. We compare XLTU with different models on our dataset, and the results demonstrate that our model XLTU achieves the SOTA results on various evaluation metrics. We make our code and dataset publicly available (https://github.com/lyfcsdo2011/XLTU).