Style transfer and classification in hebrew news items
This work addresses the problem of natural language processing for Hebrew speakers and researchers, but it is incremental as it applies existing methods to a specific domain.
The researchers tackled the challenge of modeling Hebrew, a morphologically rich language, by applying Transformer-based models like BERT to achieve state-of-the-art results in style transfer, text generation, and classification on news articles, matching performance levels seen in non-MRL languages.
Hebrew is a Morphological rich language, making its modeling harder than simpler language. Recent developments such as Transformers in general and Bert in particular opened a path for Hebrew models that reach SOTA results, not falling short from other non-MRL languages. We explore the cutting edge in this field performing style transfer, text generation and classification over news articles collected from online archives. Furthermore, the news portals that feed our collective consciousness are an interesting corpus to study, as their analysis and tracing might reveal insights about our society and discourse.