Label Projection

Label projection addresses the challenge of transferring annotations (labels) between languages or datasets, particularly crucial for training machine learning models in low-resource scenarios. Current research focuses on improving the accuracy and efficiency of label projection methods, employing techniques like constrained decoding, contextual translation with instruction-tuned language models, and optimized "mark-then-translate" strategies to minimize information loss during the projection process. These advancements are significantly impacting cross-lingual transfer learning and dataset distillation, enabling more effective training of models for various tasks, including named entity recognition and semantic parsing, across a wider range of languages and datasets.