Papers by Justin Qiu
StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples (2025.naacl-long)
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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. |
mStyleDistance: Multilingual Style Embeddings and their Evaluation (2025.findings-acl)
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| Challenge: | Multilingual StyleDistance embeddings are useful for stylistic analysis and style transfer, but they only exist for English. |
| Approach: | They propose a method that can generate style embeddings in new languages using synthetic data and a contrastive loss. |
| Outcome: | The proposed method outperforms existing style embeddings on these benchmarks and generalizes well to unseen features and languages. |