A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer

Text style transfer (TST) without parallel data has achieved some practical success. However, most of the existing unsupervised text style transfer methods suffer from (i) requiring massive amounts of non-parallel data to guide transferring different text styles. (ii) colossal performance degradation when fine-tuning the model in new domains. In this work, we propose DAML-ATM (Domain Adaptive Meta-Learning with Adversarial Transfer Model), which consists of two parts: DAML and ATM. DAML is a domain adaptive meta-learning approach to learning general knowledge in multiple heterogeneous source domains, capable of adapting to new unseen domains with a small amount of data. Moreover, we propose a new unsupervised TST approach Adversarial Transfer Model (ATM), composed of a sequence-to-sequence pre-trained language model and uses adversarial style training for better content preservation and style transfer. Results on multi-domain datasets demonstrate that our approach generalizes well on unseen low-resource domains, achieving state-of-the-art results against ten strong baselines.

Existing text style transfer (TST) methods rely on style classifiers to disentangle the text’s content and style attributes for text style transfer. While the style classifier plays a critical role in existing TST methods, there is no known investigation on its effect on the TST methods. In this paper, we conduct an empirical study on the limitations of the style classifiers used in existing TST methods. We demonstrated that the existing style classifiers cannot learn sentence syntax effectively and ultimately worsen existing TST models’ performance. To address this issue, we propose a novel Syntax-Aware Controllable Generation (SACG) model, which includes a syntax-aware style classifier that ensures learned style latent representations effectively capture the sentence structure for TST. Through extensive experiments on two popular text style transfer tasks, we show that our proposed method significantly outperforms twelve state-of-the-art methods. Our case studies have also demonstrated SACG’s ability to generate fluent target-style sentences that preserved the original content.

We introduce a simple method for text style transfer that frames style transfer as denoising: we synthesize a noisy corpus and treat the source style as a noisy version of the target style. To control for aspects such as preserving meaning while modifying style, we propose a reranking approach in the data synthesis phase. We evaluate our method on three novel style transfer tasks: transferring between British and American varieties, text genres (formal vs. casual), and lyrics from different musical genres. By measuring style transfer quality, meaning preservation, and the fluency of generated outputs, we demonstrate that our method is able both to produce high-quality output while maintaining the flexibility to suggest syntactically rich stylistic edits.

In recent years, researchers in computational linguistics have shown a growing interest in the style of text, with a specific focus on the text style transfer (TST) task. While numerous innovative methods have been proposed, it has been observed that the existing evaluations are insufficient to validate the claims and precisely measure the performance. This challenge primarily stems from rapid advancements and diverse settings of these methods, with the associated (re)implementation and reproducibility hurdles. To bridge this gap, we introduce a comprehensive benchmark for TST known as TSTBench. TSTBench includes a codebase encompassing implementations of 13 state-of-the-art algorithms and a standardized protocol for text style transfer. Based on the codebase and protocol, we have conducted thorough experiments across seven datasets, resulting in a total of 7000+ evaluations. Our work provides extensive analysis from various perspectives, explores the performance of representative baselines across various datasets, and offers insights into the task and evaluation processes to guide future research in TST.

Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer. These techniques aim to learn the disentangled representations and tweak them to modify the style of a sentence. In this paper, we propose a counterfactual-based method to modify the latent representation, by posing a ‘what-if’ scenario. This simple and disciplined approach also enables a fine-grained control on the transfer strength. We conduct experiments with the proposed methodology on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support our hypothesis.

Artistic text style transfer is the task of migrating the style from a source image to the target text to create artistic typography. Recent style transfer methods have considered texture control to enhance usability. However, controlling the stylistic degree in terms of shape deformation remains an important open challenge. In this paper, we present the first text style transfer network that allows for real-time control of the crucial stylistic degree of the glyph through an adjustable parameter. Our key contribution is a novel bidirectional shape matching framework to establish an effective glyph-style mapping at various deformation levels without paired ground truth. Based on this idea, we propose a scale-controllable module to empower a single network to continuously characterize the multi-scale shape features of the style image and transfer these features to the target text. The proposed method demonstrates its superiority over previous state-of-the-arts in generating diverse, controllable and high-quality stylized text.

Text style transfer (TST) involves altering the linguistic style of a text while preserving its style-independent content.This paper focuses on sentiment transfer, a popular TST subtask, across a spectrum of Indian languages: Hindi, Magahi, Malayalam, Marathi, Punjabi, Odia, Telugu, and Urdu, expanding upon previous work on English-Bangla sentiment transfer (Mukherjee et al., 2023a).We introduce dedicated datasets of 1,000 positive and 1,000 negative style-parallel sentences for each of these eight languages.We then evaluate the performance of various benchmark models categorized into parallel, non-parallel, cross-lingual, and shared learning approaches, including the Llama2 and GPT-3.5 large language models (LLMs).Our experiments highlight the significance of parallel data in TST and demonstrate the effectiveness of the Masked Style Filling (MSF) approach (Mukherjee et al., 2023a) in non-parallel techniques.Moreover, crosslingual and joint multilingual learning methods show promise, offering insights into selecting optimal models tailored to the specific language and task requirements.To the best of our knowledge, this work represents the first comprehensive exploration of the TST task as sentiment transfer across a diverse set of languages.

The style transfer task (here style is used in a broad “authorial” sense with many aspects including register, sentence structure, and vocabulary choice) takes text input and rewrites it in a specified target style preserving the meaning, but altering the style of the source text to match that of the target. Much of the existing research on this task depends on the use of parallel datasets. In this work we employ recent results in unsupervised cross-lingual language modeling (XLM) and machine translation to effect style transfer while treating the input data as unaligned. First, we show that adding “content embeddings” to the XLM which capture human-specified groupings of subject matter can improve performance over the baseline model. Evaluation of style transfer has often relied on metrics designed for machine translation which have received criticism of their suitability for this task. As a second contribution, we propose the use of a suite of classical stylometrics as a useful complement for evaluation. We select a few such measures and include these in the analysis of our results.

Text style transfer is an important task to render artistic texts from a reference image or style, and is widely desired in many visual creations. Previous works have brought some efficient methods for text style transfer, which facilitate users to design various artistic texts automatically. However, these works mainly focus on relatively simple text effects, and do not perform well on complex reference styles. In this paper, we propose a coarse-to-fine framework to generate exquisite texts with complex texture and structure in an unsupervised way, achieving real-time control of style scales (i.e., text stylistic degree or deformation degree). The key idea is to decouple the overall task into two steps, prototype generation and detail refinement, and explore delicate networks for each step to imitate the features at different levels. Based on this idea, in the first step, we present a novel pro-gen GAN to generate prototypes of artistic texts using the reference style, and develop a deformable module to empower the pro-gen GAN to continuously characterize the multi-scale shape features without network retraining. Furthermore, we propose a mix-attention training scheme for text style transfer, which can avoid artifacts and retain a clear text background. In the second step, we introduce two optimized networks for detail refinements. Experimental results show that the proposed method can synthesize exquisite stylized texts with complex reference styles, and surpass the state of the arts in texture reconstruction, contour imitation, and text image quality drastically. The code is available on the page: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/WendongMao/Intelligent_Typography</uri> .

Text style transfer and paraphrasing of texts are actively growing areas of NLP, dozens of methods for solving these tasks have been recently introduced. In both tasks, the system is supposed to generate a text which should be semantically similar to the input text. Therefore, these tasks are dependent on methods of measuring textual semantic similarity. However, it is still unclear which measures are the best to automatically evaluate content preservation between original and generated text. According to our observations, many researchers still use BLEU-like measures, while there exist more advanced measures including neural-based that significantly outperform classic approaches. The current problem is the lack of a thorough evaluation of the available measures. We close this gap by conducting a large-scale computational study by comparing 57 measures based on different principles on 19 annotated datasets. We show that measures based on cross-encoder models outperform alternative approaches in almost all cases. We also introduce the Mutual Implication Score (MIS), a measure that uses the idea of paraphrasing as a bidirectional entailment and outperforms all other measures on the paraphrase detection task and performs on par with the best measures in the text style transfer task.

Text style transfer, the aim of which is to convert a specific style in a given sentence to another target style while maintaining the style-independent content information of the original sentence, can face challenges when applied to non-parallel text. In this paper, we combine domain adaptation learning and an attention model to propose a new framework to accomplish the task. Domain adaptation can leverage relative information from the source domain to improve the generative model’s capacity for reconstructing data. The attention model can give the importance weights of generated words for the target style in a sentence; therefore, the generative model can concentrate on generating words with higher importance weights to accomplish text style transfer effectively. We evaluate our framework using Yelp, Amazon and Captions corpora. The results of automatic and human evaluation demonstrate the effectiveness of our framework compared with previous works under non-parallel and limited training data. The available codes are in https://github.com/mingxuan007/text-style-transfer-with-adversarial-network-and-domain-adaptation .

Style is an integral component of a sentence indicated by the choice of words a person makes. Different people have different ways of expressing themselves; however, they adjust their speaking and writing style to a social context, an audience, an interlocutor, or the formality of an occasion. Text style transfer is defined as a task of adapting and/or changing the stylistic manner in which a sentence is written, while preserving the meaning of the original sentence. A systematic review of text style transfer methodologies using deep learning is presented in this article. We point out the technological advances in deep neural networks that have been the driving force behind current successes in the fields of natural language understanding and generation. The review is structured around two key stages in the text style transfer process, namely, representation learning and sentence generation in a new style. The discussion highlights the commonalities and differences between proposed solutions as well as challenges and opportunities that are expected to direct and foster further research in the field.

Text style transfer is a task that changes the stylistic attribute while preserving content information. It can be used for various applications such as intelligent chatbot and literary works. Based on the recently appeared model, namely General Style Transformer(GST), we propose a new variant of it by replacing one of its step. The GST basically follows three steps: delete, retrieve, and generative (DRG) steps. The DRG framework is a pipeline structure, so errors of the deletion step might propagate to its following steps. To mitigate this, we modify the retrieval step to take the result sentence of the deletion step and the raw sentence as well, and results of the two sentences. It combines the results of the two sentences, so it has a chance to alleviate some errors generated by the deletion step. By experimental results with Yelp dataset, we prove the effectiveness of our approach.

Natural language generation (NLG) spans a broad range of tasks, each of which serves for specific objectives and desires different properties of generated text. The complexity makes automatic evaluation of NLG particularly challenging. Previous work has typically focused on a single task and developed individual evaluation metrics based on specific intuitions. In this paper, we propose a unifying perspective based on the nature of information change in NLG tasks, including compression (e.g., summarization), transduction (e.g., text rewriting), and creation (e.g., dialog). Information alignment between input, context, and output text plays a common central role in characterizing the generation. With automatic alignment prediction models, we develop a family of interpretable metrics that are suitable for evaluating key aspects of different NLG tasks, often without need of gold reference data. Experiments show the uniformly designed metrics achieve stronger or comparable correlations with human judgement compared to state-of-the-art metrics in each of diverse tasks, including text summarization, style transfer, and knowledge-grounded dialog.

With the rapid development of computer computing power, as an important method in the field of artificial intelligence, deep learning has amazing learning ability, especially in dealing with massive data, which makes deep learning in the fields of image recognition, image classification, natural language processing, data mining and unmanned driving, Has shown an extraordinary role. In previous studies, the style transfer algorithm has not developed well due to the poor computing power of Computer, the basic configuration of computer hardware can not meet the minimum requirements and the poor image effect after migration. However, with the development of computer hardware and the rapid change of GPU computing power, the style transfer network based on deep learning has become a hot issue in the study of style transfer in recent years. According to the research, although the traditional style transfer method can obtain the texture, color and other information of the style image, the model needs to be learned every time a new target image is generated, and the time cost during this period is very high. In this way, the trained model is not repeatable, and the generated image is often very random and can not get good results. Therefore, the emergence of style transfer methods based on deep learning solves the limitations of traditional style transfer methods. Style transfer methods based on deep learning are faster than traditional style transfer methods, and the generalization of the model is better. The style transfer algorithms of main neural networks are divided into two categories, Slow style transfer based on image iteration and fast style transfer based on model iteration. VGG network model can combine style image and content image, and greatly improve the style transfer efficiency of image.