Zero-Shot Relation Triple Extraction with Prompts for Low-Resource Languages - Ayiguli Halike; Aishan Wumaier; Tuergen Yibulayin (Applied Sciences, 2023)
Although low-resource relation extraction is vital in knowledge construction and characterization, more research is needed on the generalization of unknown relation types. To fill the gap in the study of low-resource (Uyghur) relation extraction methods, we created a zero-shot with a quick relation extraction task setup. Each triplet extracted from an input phrase consists of the subject, relation type, and object. This paper suggests generating structured texts by urging language models to provide related instances. Our model consists of two modules: relation generator and relation and triplet extractor. We use the Uyghur relation prompt in the relation generator stage to generate new synthetic data. In the relation and triple extraction stage, we use the new data to extract the relation triplets in the sentence. We use multi-language model prompts and structured text techniques to offer a structured relation prompt template. This method is the first research that extends relation triplet extraction to a zero-shot setting for Uyghur datasets. Experimental results show that our method achieves a maximum weighted average F1 score of 47.39%.