Review of Text Style Transfer Based on Deep Learning

Li, Xiangyang; Pu, Guo; Ming, Keyu; Li, Pu; Wang, Jie; Wang, Yu-Xuan

doi:10.48550/arxiv.2005.02914

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…To successfully produce such circumstancesensitive paraphrases, we need to depart from existing style transfer methodology (see Li et al, 2020 for a survey, and Madaan et al, 2020 for politeness transfer in particular). First, since we must account for arbitrary levels of misalignment, we need fine-grained control over the target stylistic level, as opposed to binary switches (e.g., from impolite to polite).…”

Section: Introductionmentioning

confidence: 99%

Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

Fussell

Danescu-Niculescu-Mizil

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Aided by technology, people are increasingly able to communicate across geographical, cultural, and language barriers. This ability also results in new challenges, as interlocutors need to adapt their communication approaches to increasingly diverse circumstances. In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance. As a case study, we focus on facilitating the accurate transmission of pragmatic intentions and introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance. We demonstrate the feasibility of this approach by evaluating our method in two realistic communication scenarios and show that it can reduce the potential for misalignment between the speaker's intentions and the listener's perceptions in both cases.

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Section: Introductionmentioning

confidence: 99%

Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

Fussell

Danescu-Niculescu-Mizil

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Semantic-aware transformation of short texts using word embeddings: An application in the Food Computing domain

Morales-Garzón

Gómez‐Romero

Martín-Bautista

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research W

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Most works in food computing focus on generating new recipes from scratch. However, there is a large number of new online recipes generated daily with a large number of users reviews, with recommendations to improve the recipe flavor and ideas to modify them. This fact encourages the use of these data for obtaining improved and customized versions. In this thesis, we propose an adaptation engine based on fine-tuning a word embedding model. We will capture, in an unsupervised way, the semantic meaning of the recipe ingredients. We will use their word embedding representations to align them to external databases, thus enriching their data. The adaptation engine will use this food data to modify a recipe into another fitting specific user preferences (e.g., decrease caloric intake or make a recipe). We plan to explore different types of recipe adaptations while preserving recipe essential features such as cuisine style and essence simultaneously. We will also modify the rest of the recipe to the new changes to be reproducible.

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Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

Fussell

Danescu-Niculescu-Mizil

2020

Preprint

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Review of Text Style Transfer Based on Deep Learning

Cited by 3 publications

References 13 publications

Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

Semantic-aware transformation of short texts using word embeddings: An application in the Food Computing domain

Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

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