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Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer

Research article (Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), 2021) · cited 38× · AI/ML
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Summary

Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer is a scholarly article[1].

Key Facts

  • Instance of: scholarly article[2]

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer. Retrieved May 24, 2026, from https://4ort.xyz/entity/thank-you-bart-rewarding-pre-trained-models-improves-formality-style-transfer
MLA “Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/thank-you-bart-rewarding-pre-trained-models-improves-formality-style-transfer.
BibTeX @misc{4ortxyz_thank-you-bart-rewarding-pre-trained-models-improves-formality-style-transfer_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer}}, year = {2026}, url = {https://4ort.xyz/entity/thank-you-bart-rewarding-pre-trained-models-improves-formality-style-transfer}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer — https://4ort.xyz/entity/thank-you-bart-rewarding-pre-trained-models-improves-formality-style-transfer (retrieved 2026-05-24)

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