Style transfer and classification in hebrew news items
Nir Weingarten
TL;DR
This paper explores advanced NLP techniques like style transfer, text generation, and classification on Hebrew news articles, leveraging recent transformer models to overcome Hebrew's morphological complexity and analyze societal discourse.
Contribution
It demonstrates the application of state-of-the-art transformer models to Hebrew news data for style transfer, generation, and classification, addressing Hebrew's morphological richness.
Findings
Achieved SOTA results in Hebrew NLP tasks
Successfully performed style transfer and text generation in Hebrew
Provided insights into societal discourse through news analysis
Abstract
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.
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Text and Document Classification Technologies
MethodsMulti-Head Attention · Attention Is All You Need · Layer Normalization · Softmax · Dropout · Weight Decay · Refunds@Expedia|||How do I get a full refund from Expedia? · Linear Layer · Dense Connections · Attention Dropout