  Relation Extraction (RE) serves as a crucial technology for transforming
unstructured text into structured information, especially within the framework
of Knowledge Graph development. Its importance is emphasized by its essential
role in various downstream tasks. Besides the conventional RE methods which are
based on neural networks and pre-trained language models, large language models
(LLMs) are also utilized in the research field of RE. However, on low-resource
languages (LRLs), both conventional RE methods and LLM-based methods perform
poorly on RE due to the data scarcity issues. To this end, this paper
constructs low-resource relation extraction datasets in 10 LRLs in three
regions (Central Asia, Southeast Asia and Middle East). The corpora are
constructed by translating the original publicly available English RE datasets
(NYT10, FewRel and CrossRE) using an effective multilingual machine
translation. Then, we use the language perplexity (PPL) to filter out the
low-quality data from the translated datasets. Finally, we conduct an empirical
study and validate the performance of several open-source LLMs on these
generated LRL RE datasets.
