| --- |
| license: unknown |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - paraphrase |
| - style-transfer |
| - parabank |
| --- |
| |
| # ParaBank Paraphrase Labeled (Style-Conditioned) |
|
|
| 200,001-row style-labeled paraphrase dataset derived from ParaBank v2, balanced at |
| 66,667 rows per style: `[FORMAL]`, `[CASUAL]` (informal), `[POLITE]`. |
|
|
| ## Columns |
| - `reference`: source English sentence |
| - `paraphrase_1`: best-ranked paraphrase from ParaBank v2 |
| - `style_label`: 0=polite, 1=formal, 2=informal |
| - plus raw formality/politeness classifier labels and confidence scores |
|
|
| ## Data Attribution |
|
|
| Derived from **ParaBank 2**: |
|
|
| > J. Edward Hu, Abhinav Singh, Nils Holzenberger, Matt Post, and Benjamin Van Durme. 2019. |
| > *Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering.* |
| > In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 44-54. |
| > https://aclanthology.org/K19-1005/ |
|
|
| **Changes made to the original data**: filtered to a 0.02-0.10 dual-condition score |
| range with heuristic quality filters (7,738,958 rows from the raw 19.72M-line corpus), |
| deduplicated, randomly sampled, labeled for formality/politeness via classifier |
| inference, and balanced to 200,001 rows (66,667 per style). Only `paraphrase_1` from |
| each original row is used. |
|
|
