Papers with style transfer tasks
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer (2022.acl-short)
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| Challenge: | Text style transfer is a text generation task where a given sentence must be rewritten changing its style while preserving its meaning. |
| Approach: | They propose a modular approach for multilingual formality transfer using machine translated data and gold aligned English sentences. |
| Outcome: | The proposed approach achieves competitive performance without monolingual task-specific parallel data and can be applied to other style transfer tasks as well as to other languages. |
A Recipe for Arbitrary Text Style Transfer with Large Language Models (2022.acl-short)
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| Challenge: | augmented zero-shot learning is a prompting method that allows large language models to perform zero-shoot text style transfer to arbitrary styles, without any model fine-tuning or exemplars in the target style. |
| Approach: | They propose a prompting method that frames style transfer as a sentence rewriting task and requires only a natural language instruction. |
| Outcome: | The proposed method is based on a large language model and is shown to perform on standard style transfer tasks and arbitrary transformations. |
NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer (2021.findings-acl)
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| Challenge: | Autoregressive text style transfer models often ignore part of the source sentence and generate some irrelevant words with strong styles. |
| Approach: | They propose a non-autoregressive generator for unsupervised text style transfer which explicitly models word alignments to suppress irrelevant words. |
| Outcome: | The proposed generator significantly improves performance and provides explainable word alignments. |
Politeness Transfer: A Tag and Generate Approach (2020.acl-main)
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Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, Shrimai Prabhumoye
| Challenge: | Prior work on text style transfer has not focused on politeness as a style transfer task and we argue that defining it is cumbersome. |
| Approach: | They propose a task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. |
| Outcome: | The proposed model outperforms state-of-the-art methods on content preservation and style transfer accuracy. |
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus (N19-1)
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| Challenge: | Existing methods for text style transfer have demonstrated considerable success, but a parallel corpus may not always be available for a transfer task. |
| Approach: | They propose a text style transfer model that uses an attention-based encoder-decoder to transfer a sentence from the source style to the target style. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two different style transfer tasks. |
Domain Adaptive Text Style Transfer (D19-1)
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| Challenge: | Text style transfer without parallel data is a promising method for learning, but in the scenario where less data is available, it may yield poor performance. |
| Approach: | They propose to leverage available data to learn domain-adaptive text style transfer models . they evaluate two style transfer tasks where only limited non-parallel data is available . |
| Outcome: | The proposed models learn from the source domain to: (i) distinguish stylized information and generic content information; (ii) maximally preserve content information and (iv) adaptively transfer the styles in a domain-aware manner. |
Generic resources are what you need: Style transfer tasks without task-specific parallel training data (2021.emnlp-main)
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| Challenge: | Text style transfer is a task aimed at converting a text of one style into another while preserving its content. |
| Approach: | They propose a multi-step procedure which builds on a generic pre-trained sequence-to-sequence model and an iterative back-translation approach to train two models in a transfer direction. |
| Outcome: | The proposed method outperforms existing unsupervised approaches on the two most popular style transfer tasks: formality transfer and polarity swap. |
Plug and Play Autoencoders for Conditional Text Generation (2020.emnlp-main)
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| Challenge: | Text autoencoders are used for conditional generation tasks such as style transfer. |
| Approach: | They propose a plug-and-play method where any pretrained autoencoder can be used and only requires learning a mapping within the embedding space. |
| Outcome: | The proposed method performs better than or comparable to strong baselines while being up to four times faster. |
MORL-Prompt: An Empirical Analysis of Multi-Objective Reinforcement Learning for Discrete Prompt Optimization (2024.findings-emnlp)
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| Challenge: | Current RL-based prompt tuning techniques focus on maximizing the average of reward functions, which does not necessarily lead to prompts that achieve balance across rewards. |
| Approach: | They compare RL-based discrete prompt optimization techniques with a new set of target language models to find prompts that maximize an average of rewards. |
| Outcome: | The proposed methods perform better on two NLP tasks, style transfer and machine translation, and achieve a better balance of all rewards. |