| Challenge: | Existing studies on text style transfer focus on altering sentiment words to preserve attribute-independent information. |
| Approach: | They propose a Dual-Generator network architecture for text Style Transfer using two generators. |
| Outcome: | The proposed model performs better than existing models on Yelp and IMDb datasets. |
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Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer (2023.findings-emnlp)
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| Challenge: | Existing studies explore performing text style transfer on attributes like age, gender, formality, politeness, and formality. |
| Approach: | They propose a framework that freezes the pre-trained model’s original parameters and enables the development of a multiple-attribute text style transfer model. |
| Outcome: | The proposed model outperforms state-of-the-art models on sentiment transfer and multiple-attribute transfer tasks with significantly less computational resources. |
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation (2020.coling-industry)
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Pawel Bujnowski, Kseniia Ryzhova, Hyungtak Choi, Katarzyna Witkowska, Jaroslaw Piersa, Tymoteusz Krumholc, Katarzyna Beksa
| Challenge: | a limited amount of style data is needed for text style transfer, but there are no convincing methods for evaluating them. |
| Approach: | They propose an efficient method for neutral-to-style transformation using the transformer framework. |
| Outcome: | The proposed method can train neutral-to-style transformation models using large paraphrases and a small style transfer corpus. |
Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG (P19-1)
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| Challenge: | Neural natural language generation (NNLG) models generate syntactically correct utterances from structured inputs without needing hand-crafted rules or templates. |
| Approach: | They propose a method for generating a corpus of parallel meaning representations with rich style markup using freely available and naturally descriptive user reviews. |
| Outcome: | The proposed method can be scalably reused to generate NLG datasets for other domains. |
Formality Style Transfer for Noisy, User-generated Conversations: Extracting Labeled, Parallel Data from Unlabeled Corpora (D19-55)
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| Challenge: | Typical datasets used for style transfer in NLP contain aligned pairs of two opposite extremes of a style. |
| Approach: | They propose a technique to derive a dataset of aligned pairs from an unlabeled corpus by using an auxiliary dataset, allowing for in-domain training. |
| Outcome: | The proposed method significantly outperforms OpenNMT’s Seq2Seq model trained on the Yahoo Formality Dataset and 6 novel datasets. |
Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)
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| Challenge: | Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization. |
| Approach: | They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles. |
| Outcome: | The proposed model outperforms competing models in three domains with diverse topics and varying language styles. |
“Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer (D19-1)
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| Challenge: | Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information. |
| Approach: | They propose a new approach to rewriting sentences to a target style in the absence of parallel style corpora by exploiting the Transformer. |
| Outcome: | The proposed method outperforms state-of-the-art systems across 5 datasets on sentiment, gender and political slant transfer. |
Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training (D19-1)
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| Challenge: | Generative adversarial network (GAN) is a popular model for text style transfer . but, training GAN often suffers from mode collapse problem, which causes that the transferred text is little related to the original text. |
| Approach: | They propose a non-parallel text style transfer model with a word-level conditional architecture and a two-phase training procedure to maintain style-unrelated words while changing others. |
| Outcome: | The proposed model outperforms state-of-the-art models on three real-world datasets in transfer accuracy and fluency. |
Audience-Centric Natural Language Generation via Style Infusion (2022.findings-emnlp)
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| Challenge: | Existing approaches to text style transfer (TST) with large volumes of parallel or non-parallel data are limiting for two reasons: it is difficult to collect large volumes and some stylistic objectives are hard to define without audience feedback. |
| Approach: | They propose a task of style infusion - infusing stylistic preferences of audiences into pretrained language generation models by leveraging pairwise human judgments to bootstrap a style analysis model and augment a seed set of judgments. |
| Outcome: | The proposed method generates compelling stylized examples with generic text prompts while balancing fluency and style adoption. |
Style-Specific Neurons for Steering LLMs in Text Style Transfer (2024.emnlp-main)
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| Challenge: | Existing LLMs tend to prioritize preserving original meaning over enhancing stylistic differences in TST. |
| Approach: | They propose a novel approach to steering LLMs using style-specific neurons in TST. |
| Outcome: | Empirical results show that the proposed method improves the fluency of the generated text. |
StyleDGPT: Stylized Response Generation with Pre-trained Language Models (2020.findings-emnlp)
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| Challenge: | Existing methods for generating responses following a desired style are lacking of parallel data for training. |
| Approach: | They propose a KL loss and a style classifier to fine-tune response generation . they show that their model can significantly outperform state-of-the-art methods . |
| Outcome: | The proposed model outperforms state-of-the-art models in style consistency and contextual coherence with two public datasets. |