Source Language

Source language selection significantly impacts the success of various natural language processing (NLP) and machine learning tasks, particularly in cross-lingual transfer and code generation. Current research focuses on optimizing source language choice for improved performance in low-resource scenarios, leveraging multilingual models and exploring techniques like contrastive learning and multi-armed bandit algorithms to select optimal source languages based on factors such as language contact and structural similarity. These advancements are crucial for bridging the language gap in applications like machine translation, code generation for diverse programming languages, and cross-lingual summarization, ultimately improving the accessibility and efficiency of these technologies.