Monolingual Corpus

Monolingual corpora, large collections of text in a single language, are crucial resources for advancing natural language processing (NLP), particularly for low-resource languages lacking parallel corpora (paired texts in multiple languages). Current research focuses on leveraging monolingual data to improve multilingual models through techniques like data augmentation (synthesizing new data from existing resources), cross-lingual transfer learning (applying knowledge learned from one language to another), and improved training strategies that incorporate knowledge-based alignments or exploit lexical overlap between related languages. This work is significant because it addresses the data scarcity problem hindering NLP development for many languages, enabling advancements in machine translation, speech synthesis, and other NLP applications.