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

Rao, Sudha; Tetreault, Joel

doi:10.18653/v1/n18-1012

Cited by 296 publications

(481 citation statements)

References 28 publications

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“…A related task is to modify or paraphrase text data to obfuscate gender as in (Reddy and Knight 2016). Another closely related work is to change the style of the text to different levels of formality as in (Rao and Tetreault 2018).…”

Section: Approaches Using Non-parallel Datamentioning

confidence: 99%

“…On the other hand, the Formality dataset was part of the Yahoo Answers corpus L6, which was labeled in (Rao and Tetreault 2018). Table 1 shows that the Gender dataset consists of 2.889M sentences per class.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…The dataset was split into train, test, dev and classifier set per class with 1.334M, 267.2K, 2.246K, 1.288M samples respectively. From Table 2 we can see that the Formality dataset which is a part of GY-AFC dataset (Rao and Tetreault 2018) consists of 81.844K sentences per class. Out of 81.844K sentences the dataset was split into train, test, dev and classifier set per class with 51.967K, 1.332K, 2.788K, 52.595K samples respectively.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Style is an essential ingredient of effective communication, and by varying it one can convey more or less information than the literal meaning of words (Hovy 1988;Rao and Tetreault 2018). Therefore reasoning about language style is a foundational capability needed to build successful NLP/G systems.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the existing style transfer techniques have focussed on individual and granular styles; for example, altering the sentiment of a review from negative to positive style, transferring a tweet from male to female style, and converting the style of text from one author to other (Li et al 2018;Reddy and Knight 2016;Jhamtani et al 2017;Rabinovich et al 2016). More recent work has attempted to transfer individual soft styles such as impolite to polite and informal to formal (Aubakirova and Bansal 2016;Rao and Tetreault 2018). In practice however, we are often interested in incorporating multiple styles in the same content.…”

Section: Introductionmentioning

confidence: 99%

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Fair Transfer of Multiple Style Attributes in Text

Dabas

Madaan

Arya

et al. 2019

2019 Grace Hopper Celebration India (GHCI)

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To preserve anonymity and obfuscate their identity on online platforms users may morph their text and portray themselves as a different gender or demographic. Similarly, a chatbot may need to customize its communication style to improve engagement with its audience. This manner of changing the style of written text has gained significant attention in recent years. Yet these past research works largely cater to the transfer of single style attributes. The disadvantage of focusing on a single style alone is that this often results in target text where other existing style attributes behave unpredictably or are unfairly dominated by the new style. To counteract this behavior, it would be nice to have a style transfer mechanism that can transfer or control multiple styles simultaneously and fairly. Through such an approach, one could obtain obfuscated or written text incorporated with a desired degree of multiple soft styles such as female-quality, politeness, or formalness. In this work, we demonstrate that the transfer of multiple styles cannot be achieved by sequentially performing multiple single-style transfers. This is because each single styletransfer step often reverses or dominates over the style incorporated by a previous transfer step. We then propose a neural network architecture for fairly transferring multiple style attributes in a given text. We test our architecture on the Yelp data set to demonstrate our superior performance as compared to existing one-style transfer steps performed in a sequence.

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Section: Approaches Using Non-parallel Datamentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fair Transfer of Multiple Style Attributes in Text

Dabas

Madaan

Arya

et al. 2019

2019 Grace Hopper Celebration India (GHCI)

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Search and learning for unsupervised text generation

Mou

2022

AI Magazine

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With the advances of deep learning techniques, text generation is attracting increasing interest in the artificial intelligence (AI) community, because of its wide applications and because it is an essential component of AI. Traditional text generation systems are trained in a supervised way, requiring massive labeled parallel corpora. In this paper, I will introduce our recent work on search and learning approaches to unsupervised text generation, where a heuristic objective function estimates the quality of a candidate sentence, and discrete search algorithms generate a sentence by maximizing the search objective. A machine learning model further learns from the search results to smooth out noise and improve efficiency. Our approach is important to the industry for building minimal viable products for a new task; it also has high social impacts for saving human annotation labor and for processing low‐resource languages. Link to video abstract: https://youtu.be/Xir1e9g6oIc

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Reinforced Rewards Framework for Text Style Transfer

Sancheti

Krishna

Srinivasan³

et al. 2020

Lecture Notes in Computer Science

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Style transfer deals with the algorithms to transfer the stylistic properties of a piece of text into that of another while ensuring that the core content is preserved. There has been a lot of interest in the field of text style transfer due to its wide application to tailored text generation. Existing works evaluate the style transfer models based on content preservation and transfer strength. In this work, we propose a reinforcement learning based framework that directly rewards the framework on these target metrics yielding a better transfer of the target style. We show the improved performance of our proposed framework based on automatic and human evaluation on three independent tasks: wherein we transfer the style of text from formal to informal, high excitement to low excitement, modern English to Shakespearean English, and vice-versa in all the three cases. Improved performance of the proposed framework over existing state-of-the-art frameworks indicates the viability of the approach.

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

Cited by 296 publications

References 28 publications

Fair Transfer of Multiple Style Attributes in Text

Fair Transfer of Multiple Style Attributes in Text

Search and learning for unsupervised text generation

Reinforced Rewards Framework for Text Style Transfer

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