Parallel Text

Parallel text, consisting of texts in multiple languages that are mutual translations, is crucial for machine translation and cross-lingual natural language processing. Current research focuses on improving the efficiency of parallel text acquisition through smart crawling techniques and data augmentation methods, as well as optimizing parallel processing of large language models using techniques like tensor parallelism and kernel fusion to accelerate training and inference. These advancements are vital for bridging language barriers and improving the performance of various NLP applications, particularly for low-resource languages where parallel data is scarce.