Target Language

Target language research focuses on bridging the gap between high-resource and low-resource languages in various natural language processing tasks. Current efforts concentrate on improving cross-lingual transfer learning, often employing transformer-based models and techniques like continual pre-training, data augmentation (including pseudo-data generation), and model adaptation strategies to enhance performance in target languages with limited data. This research is crucial for expanding the accessibility and utility of NLP technologies globally, impacting fields such as machine translation, question answering, and dialogue systems.