Summary of Styledistance: Stronger Content-independent Style Embeddings with Synthetic Parallel Examples, by Ajay Patel et al.
StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples
by Ajay Patel, Jiacheng Zhu, Justin Qiu, Zachary Horvitz, Marianna Apidianaki, Kathleen McKeown, Chris Callison-Burch
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content. However, the contrastive triplets often used for training these representations may vary in both style and content, leading to potential content leakage in the representations. To address this issue, researchers introduce StyleDistance, a novel approach to training stronger content-independent style embeddings. They create a synthetic dataset of near-exact paraphrases with controlled style variations and produce positive and negative examples across 40 distinct style features for precise contrastive learning. The quality of their synthetic data and embeddings is assessed through human and automatic evaluations. Results show that StyleDistance enhances the content-independence of style embeddings, which generalize to real-world benchmarks and outperform leading style representations in downstream applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers have been trying to make computers understand what makes different writings styles unique, like formal or casual. But they’ve found a problem – when training these computer models, some examples might have similar writing styles but also be about the same topic. This could lead to mistakes. To fix this, the researchers created new fake data that’s very similar but with different styles. They used this data to train their model, which they call StyleDistance. It seems to work well and can even beat other models at tasks like language translation. |
Keywords
» Artificial intelligence » Synthetic data » Translation
