Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer

Nangi, Sharmila Reddy; Chhaya, Niyati; Khosla, Sopan; Kaushik, Nikhil; Nyati, Harshit

doi:10.18653/v1/2021.acl-short.7

Cited by 14 publications

(9 citation statements)

References 20 publications

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“…Among them, positive-negative emotional style transfer is generally a subjective evaluation of something 6 . Informal -formal style transfer focuses on formal normalization of text 7 . Complexsimple language style transfer is applied in auxiliary reading, converting complex long sentences into short sentences that are easy to read and understand 8 .…”

Section: Overview and Challenges Of Text Style Transfermentioning

confidence: 99%

A review of unsupervised text style transfer based on deep learning

Guo,

Rao

2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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Text style transfer is mainly to modify the text style to suit various application scenarios without changing the semantic meaning of the text, which is a great significant issue in natural language processing. To expedite research progress, this survey conducts a systematic review of existing literature. Drawing from a vast body of research, this survey first extracts the essential connotations of style information in text of varying granularity across different tasks, and then provide a clear definition of text style transfer, summarize the main challenges at present and comprehensively codify and discuss the current datasets used for evaluation, as well as their indicators. Moreover, this survey compares the mechanisms, advantages, and shortcomings of unsupervised classical methods across word, sentence, and paragraph levels. Finally, the future directions of fighting are also given, hoping to facilitate more comprehensive solutions.

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Section: Overview and Challenges Of Text Style Transfermentioning

confidence: 99%

A review of unsupervised text style transfer based on deep learning

Guo,

Rao

2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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“…l https://github.com/facebookresearch/EmpatheticDialogues Though not directly formulated in emotion-related terms, an effort of emotion style transfer can be found in Nangi et al (2021). Therefore, the produced paraphrases display a different degree of excitement than the original texts, mirroring the notion of arousal in the continuous models of emotions.…”

Section: Sarcasticmentioning

confidence: 99%

From theories on styles to their transfer in text: Bridging the gap with a hierarchical survey

2022

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Humans are naturally endowed with the ability to write in a particular style. They can, for instance, rephrase a formal letter in an informal way, convey a literal message with the use of figures of speech or edit a novel by mimicking the style of some well-known authors. Automating this form of creativity constitutes the goal of style transfer. As a natural language generation task, style transfer aims at rewriting existing texts, and specifically, it creates paraphrases that exhibit some desired stylistic attributes. From a practical perspective, it envisions beneficial applications, like chatbots that modulate their communicative style to appear empathetic, or systems that automatically simplify technical articles for a non-expert audience. Several style-aware paraphrasing methods have attempted to tackle style transfer. A handful of surveys give a methodological overview of the field, but they do not support researchers to focus on specific styles. With this paper, we aim at providing a comprehensive discussion of the styles that have received attention in the transfer task. We organize them in a hierarchy, highlighting the challenges for the definition of each of them and pointing out gaps in the current research landscape. The hierarchy comprises two main groups. One encompasses styles that people modulate arbitrarily, along the lines of registers and genres. The other group corresponds to unintentionally expressed styles, due to an author’s personal characteristics. Hence, our review shows how these groups relate to one another and where specific styles, including some that have not yet been explored, belong in the hierarchy. Moreover, we summarize the methods employed for different stylistic families, hinting researchers towards those that would be the most fitting for future research.

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“…Setting. We follow the common non-parallel text style transfer setting as in (Nangi et al, 2021), where each text sample x is associated with a style label y. Table 3.…”

Section: Few-shot Text-style Transfermentioning

confidence: 99%

“…We compare five style transfer baselines: i) R-VAE-AVG (John et al, 2018a) learns the disentangled label and content representations. ii) R-VAE-CF (Nangi et al, 2021), on the base of R-VAE-AVG , uses a counterfactual reasoning module to control the generation of label representations. iii) ZF (Smith et al, 2019) is a back-translation model, which aims to deal with the zero-shot text transfer problem.…”

Section: Few-shot Text-style Transfermentioning

confidence: 99%

Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation

Li¹,

Qu²,

Xu³

et al. 2022

Preprint

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In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR , for conditional natural language generation with none or a handful of task-specific labeled examples. In order to improve compositional generalization, our model performs disentangled representation learning by introducing a prior for the latent content space and another prior for the latent label space. We show both empirically and theoretically that the conditional priors can already disentangle representations even without specific regularizations as in the prior work. We can also sample diverse content representations from the content space without accessing data of the seen tasks, and fuse them with the representations of novel tasks for generating diverse texts in the low-resource settings. Our extensive experiments demonstrate the superior performance of our model over competitive baselines in terms of i) data augmentation in continuous zero/fewshot learning, and ii) text style transfer in both zero/few-shot settings.

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Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer

Cited by 14 publications

References 20 publications

A review of unsupervised text style transfer based on deep learning

A review of unsupervised text style transfer based on deep learning

From theories on styles to their transfer in text: Bridging the gap with a hierarchical survey

Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation

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