Papers by Zachary Horvitz
StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples (2025.naacl-long)
Copied to clipboard
Ajay Patel, Jiacheng Zhu, Justin Qiu, Zachary Horvitz, Marianna Apidianaki, Kathleen McKeown, Chris Callison-Burch
| Challenge: | Existing methods for embedding text are limited by the imperfect nature of data acquired under such assumptions. |
| Approach: | They propose a new approach to training stronger content-independent style embeddings using a synthetic dataset of near-exact paraphrases with controlled style variations. |
| Outcome: | The proposed model outperforms existing methods in real-world benchmarks and outperformed leading style representations in downstream applications. |
TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for text style transfer rely on few-shot capabilities of large language models or complex controllable text generation approaches that are inefficient and underperform on fluency metrics. |
| Approach: | They propose a lightweight but effective approach which leverages a small language model and pre-trained authorship embeddings to perform efficient, few-shot text style transfer. |
| Outcome: | The proposed method outperforms strong approaches such as GPT-4 and performs form attribute style transfer with automatic and human evaluations. |