Publication

StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples

We introduce STYLE DISTANCE, a novel method for training style representations that effectively separate writing style from content by leveraging a synthetic dataset of paraphrases with controlled style variations. Our approach achieves superior content-independent embeddings, validated through human and automated evaluations, and outperforms existing methods on real-world benchmarks. Continue reading StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples