Language Similarity

Language similarity research aims to quantify and understand relationships between languages, facilitating cross-lingual applications in natural language processing. Current research focuses on developing robust methods for measuring similarity, leveraging techniques like large language models, convolutional generative adversarial networks, and linear discriminative learners, often incorporating linguistic features such as morphology, syntax, and semantics. These advancements are crucial for improving machine translation, cross-lingual transfer learning, and other NLP tasks, particularly for low-resource languages where data is scarce, and for enhancing our understanding of language evolution and typology.