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GYAFC (Grammarly’s Yahoo Answers Formality Corpus)

Introduced by Sudha Rao et al. in Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer17 Mar 2018 archive 2025-07-28

Grammarly’s Yahoo Answers Formality Corpus (GYAFC) is the largest dataset for any style containing a total of 110K informal / formal sentence pairs.

Yahoo Answers is a question answering forum, contains a large number of informal sentences and allows redistribution of data. The authors used the Yahoo Answers L6 corpus to create the GYAFC dataset of informal and formal sentence pairs. In order to ensure a uniform distribution of data, they removed sentences that are questions, contain URLs, and are shorter than 5 words or longer than 25. After these preprocessing steps, 40 million sentences remain.

The Yahoo Answers corpus consists of several different domains like Business, Entertainment & Music, Travel, Food, etc. Pavlick and Tetreault formality classifier (PT16) shows that the formality level varies significantly across different genres. In order to control for this variation, the authors work with two specific domains that contain the most informal sentences and show results on training and testing within those categories. The authors use the formality classifier from PT16 to identify informal sentences and train this classifier on the Answers genre of the PT16 corpus which consists of nearly 5,000 randomly selected sentences from Yahoo Answers manually annotated on a scale of -3 (very informal) to 3 (very formal). They find that the domains of Entertainment & Music and Family & Relationships contain the most informal sentences and create the GYAFC dataset using these domains.

Source: Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer

Benchmarks archive 2025-07-28

All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Formality Style Transfer GYAFC Consistency Training BLEU 81.37 Semi-Supervised Formality Style Transfer with... aolius/semi-fst 1 Compare
Style Transfer GYAFC BART (TextBox 2.0) Accuracy 94.37 TextBox 2.0: A Text Generation Library with Pre-trained... RUCAIBox/TextBox 1 Compare

Papers archive 2025-07-28

2 shown of 2 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 103. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
TextBox 2.0: A Text Generation Library with Pre-trained Language Models 1 1 26 Dec 2022 ran 1 of 1 samples (0 unverified)
Semi-Supervised Formality Style Transfer with Consistency Training 1 1 25 Mar 2022 ran 1 of 1 samples (0 unverified)

Dataset loaders archive 2025-07-28

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

Custom (research-only)

Modalities archive 2025-07-28

Languages archive 2025-07-28

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Variants archive 2025-07-28

  • GYAFC

1 variant name, as the archive lists them.

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