Cross Lingual Natural Language Inference

Cross-lingual natural language inference (CLNLI) is a crucial task in the field of natural language processing, aiming to leverage data and models from resource-rich languages (such as English) to address natural language inference problems in resource-poor languages. The goal of CLNLI is to improve the performance of low-resource languages in semantic understanding and logical reasoning through transfer learning and multilingual representation, thereby promoting the development of applications in multilingual information processing and cross-lingual knowledge sharing.