Text Style Research Articles

Subjective Modality and Means of its Expression in a Journalistic Style

This article is dedicated to the study of one of the categories of the sentence — modality. Many domestic linguists define this category as the attitude of the speaker to reality. Representing a complex phenomenon,it does not have a clear definition and according to many linguists requires clarification. The article presents the main provisions from the works of V. V. Vinogradov, who compiled a voluminous list of means denoting modal meanings, and G. A. Zolotova. The definition of the concepts of objective and subjective modality is given according to the «Russian grammar» (of 1980) and the «Linguistic encyclopedic dictionary». The means of expressing subjective modality are analyzed in journalistic style texts by means of an example of online newspaper «Culture». Cases of expression are identified and considered at the level of simple and complex sentences. It is noted that the subjective modality is associated with the use of lexical repetition, phrases, introductory words, separate, interjections, words that cause desire, possibility, intention or non-union complex sentences, complex sentences, unions and allied words. It was found in the article that the subjective modality is mostly expressed by using units of lexical and syntactic levels.

Memory-enhanced text style transfer with dynamic style learning and calibration

Memory-enhanced text style transfer with dynamic style learning and calibration

Self-supervised text de-stylization based on BERT

Recent advancements in Natural Language Processing (NLP) have ushered in a new era of textual style transfer (TST), a domain aimed at altering textual attributes such as tone and sentiment while preserving the content's essence. This study introduces a creative framework that employs a dual-component architecture consisting of a classifier and a generator to achieve text de-stylization, particularly sentiment neutralization. The classifier, built upon the Bidirectional Encoder Representations from Transformers (BERT) model, serves as a dynamic loss function guiding the generator, constructed on a Transformer-based encoder-decoder framework, to produce sentiment-neutral text. Our method leverages a self-supervised mechanism, enabling the generation of target text without reliance on parallel corpora, thereby addressing the limitations of existing TST methodologies. We preprocessed datasets from Stanford Sentiment Treebank-5 (SST-5) and Internet Movie Database (IMDb) movie reviews and employed them for training the classifier and generator, respectively. Preliminary results demonstrate the model's proficiency in preserving semantic integrity while effectively neutralizing sentiment. Future work envisions expanding this framework to enable text stylization across a spectrum of discursive contexts, enhanced by deep learning architectures and an iterative feedback mechanism for user-driven refinement.

ParaGuide: Guided Diffusion Paraphrasers for Plug-and-Play Textual Style Transfer

Textual style transfer is the task of transforming stylistic properties of text while preserving meaning. Target "styles" can be defined in numerous ways, ranging from single attributes (e.g. formality) to authorship (e.g. Shakespeare). Previous unsupervised style-transfer approaches generally rely on significant amounts of labeled data for only a fixed set of styles or require large language models. In contrast, we introduce a novel diffusion-based framework for general-purpose style transfer that can be flexibly adapted to arbitrary target styles at inference time. Our parameter-efficient approach, ParaGuide, leverages paraphrase-conditioned diffusion models alongside gradient-based guidance from both off-the-shelf classifiers and strong existing style embedders to transform the style of text while preserving semantic information. We validate the method on the Enron Email Corpus, with both human and automatic evaluations, and find that it outperforms strong baselines on formality, sentiment, and even authorship style transfer.

Text Image Inpainting via Global Structure-Guided Diffusion Models

Real-world text can be damaged by corrosion issues caused by environmental or human factors, which hinder the preservation of the complete styles of texts, e.g., texture and structure. These corrosion issues, such as graffiti signs and incomplete signatures, bring difficulties in understanding the texts, thereby posing significant challenges to downstream applications, e.g., scene text recognition and signature identification. Notably, current inpainting techniques often fail to adequately address this problem and have difficulties restoring accurate text images along with reasonable and consistent styles. Formulating this as an open problem of text image inpainting, this paper aims to build a benchmark to facilitate its study. In doing so, we establish two specific text inpainting datasets which contain scene text images and handwritten text images, respectively. Each of them includes images revamped by real-life and synthetic datasets, featuring pairs of original images, corrupted images, and other assistant information. On top of the datasets, we further develop a novel neural framework, Global Structure-guided Diffusion Model (GSDM), as a potential solution. Leveraging the global structure of the text as a prior, the proposed GSDM develops an efficient diffusion model to recover clean texts. The efficacy of our approach is demonstrated by thorough empirical study, including a substantial boost in both recognition accuracy and image quality. These findings not only highlight the effectiveness of our method but also underscore its potential to enhance the broader field of text image understanding and processing. Code and datasets are available at: https://github.com/blackprotoss/GSDM.

Prot2Text: Multimodal Protein’s Function Generation with GNNs and Transformers

In recent years, significant progress has been made in the field of protein function prediction with the development of various machine-learning approaches. However, most existing methods formulate the task as a multi-classification problem, i.e. assigning predefined labels to proteins. In this work, we propose a novel approach, Prot2Text, which predicts a protein's function in a free text style, moving beyond the conventional binary or categorical classifications. By combining Graph Neural Networks(GNNs) and Large Language Models(LLMs), in an encoder-decoder framework, our model effectively integrates diverse data types including protein sequence, structure, and textual annotation and description. This multimodal approach allows for a holistic representation of proteins' functions, enabling the generation of detailed and accurate functional descriptions. To evaluate our model, we extracted a multimodal protein dataset from SwissProt, and demonstrate empirically the effectiveness of Prot2Text. These results highlight the transformative impact of multimodal models, specifically the fusion of GNNs and LLMs, empowering researchers with powerful tools for more accurate function prediction of existing as well as first-to-see proteins.

Adaptive Prompt Routing for Arbitrary Text Style Transfer with Pre-trained Language Models

Recently, arbitrary text style transfer (TST) has made significant progress with the paradigm of prompt learning. In this paradigm, researchers often design or search for a fixed prompt for any input. However, existing evidence shows that large language models (LLMs) are prompt-sensitive and it is sub-optimal to apply the same prompt to any input for downstream TST tasks. Besides, the prompts obtained by searching are often unreadable and unexplainable to humans. To address these issues, we propose an Adaptive Prompt Routing (APR) framework to adaptively route prompts from a human-readable prompt set for various input texts and given styles. Specifically, we first construct a candidate prompt set of diverse and human-readable prompts for the target style. This set consists of several seed prompts and their variants paraphrased by an LLM. Subsequently, we train a prompt routing model to select the optimal prompts efficiently according to inputs. The adaptively selected prompt can guide the LLMs to perform a precise style transfer for each input sentence while maintaining readability for humans. Extensive experiments on 4 public TST benchmarks over 3 popular LLMs (with parameter sizes ranging from 1.5B to 175B) demonstrate that our APR achieves superior style transfer performances, compared to the state-of-the-art prompt-based and fine-tuning methods. The source code is available at https://github.com/DwyaneLQY/APR

STYLE IN TRANSLATION AS A PARAMETER IN TQA: A PRAGMATIC PERSPECTIVE

Style in translation can be a parameter in translation quality assessment (TQA). The style in translation refers to the translation having the same perspective as the source text. In assessing style in translation, translation does not only focus on the meaning but also the relevance of utterance. The article discusses the translation style based on pragmatic studies. The study used the utterances in the tourism text Solo Calendar of Event 2018 as data since the text has a communication function. The function of the text should be represented in the translation to achieve the same perspective of translation. The style in translation also considers the translator's effort to achieve the equivalence in meaning and relevance of utterance. The style in translation of a pragmatic perspective requires a relevance of communication. The study indicates that the there are two relevance degree, namely translation with strong and weak relevance. Translation with strong relevance indicates that translation can preserve the style of source text. The maintaining of style of source text contribute to the high quality of translation.

A Translation to Bring Anyone to Their Chineknees

ABSTRACT: This essay is based on an in-depth transtextual study of two independently published Chinese versions of the first two chapters of Finnegans Wake; the respective translations are Dai Congrong's 2012 simplified Chinese version (as contained in Finnegans Wake , Volume 1), and Liang Sun-chieh's 2017 traditional character rendering of the first two chapters of Book I, titled A Selected Translation of "Finnegans Wake." Throughout, these two ground-breaking target-language texts are compared with regard to the extent to which they have remained true to the content, style, and spirit of the original text, their overall readability, their respective stylistic approaches to Wakean wordplay and humor, their domesticating and annotative efforts to ensure greater accessibility for Chinese readers, and, finally, their creative endeavors to enhance the overall reading experience in translation.

Foreground and background separated image style transfer with a single text condition

Traditional image-based style transfer requires additional reference style images, making it less user-friendly. Text-based methods are more convenient but suffer from issues like slow generation, unclear content, and poor quality. In this work, we propose a new style transfer method SA2-CS (means Semantic-Aware and Salient Attention CLIPStyler), which is based on the Comparative Language Image Pretraining (CLIP) model and a salient object detection network. Masks obtained from the salient object detection network are utilized to guide the style transfer process, and various strategies are employed to optimize according to different masks. Adequate experiments with diverse content images and style text descriptions were conducted, demonstrating our method's advantages: the network is easily trainable and converges rapidly; it achieves stable, superior generation results compared to other methods. Our approach addresses over-stylization issues in the foreground, enhances foreground-background contrast, and enables precise control over style transfer in various semantic regions.

Translation of Scientific Texts into English as Intercultural Mediation

With English functioning as a lingua franca in the academic world, many non-Anglophone scholars wish to publish their articles in English-medium international journals and seek professional assistance with translating them into English. Translators facing this task may encounter several issues stemming from cross-cultural differences in the style and structure of academic texts. While linguistic errors alone rarely result in rejections, deviations from Anglo–American conventions in scientific writing may even lead to the reviewers questioning the author’s competence as a scientist. For example, there are significant differences between two intellectual traditions: Teutonic, which has influenced the Polish style of academic communication, and Saxonic, which has shaped Anglo–American standards. As a result, introductions written by Polish scholars often do not meet the requirements of English-medium international journals. This may inadvertently place Polish authors in a disadvantaged position. It is therefore important that translators should know how to approach these differences when they encounter them. Drawing on existing literature, this paper calls for an inquiry into the role of the translator in these circumstances, including the scope of their responsibilities and strategies for dealing with potential problems.

Using colloquial language in prose fiction texts: An exploratory study

Prose fiction texts have an aesthetic function, which seeks to create a sense of aesthetic appreciation through means of language style amongst other devices. Bearing this in mind, it is the author’s belief that using colloquial language in a prose fiction text may negatively impact language style of the text and acquisition of the foreign language. Motivated by this conviction, this study addresses two main questions. First, how does use of colloquial language impact language style of a prose fiction text? Second, how does use of colloquial language impact language acquisition in relation to foreign and second English learners? For data gathering purposes, the author of this paper created and distributed a questionnaire comprising ten items. Including three groups: English as a Foreign Language (EFL) students; EFL university instructors; and adult readers, the questionnaire was addressed to a total of one hundred and sixty-six subjects in Dubai. Moreover, the researcher conducted a semi-structured interview involving six subjects. According to the statistical results, most of the respondents and interviewees believed that including colloquial language in prose fiction texts would negatively impact language style of the text and acquisition of the foreign language.

Exploring Style-Robust Scene Text Detection via Style-Aware Learning

Although current scene text detectors achieve remarkable accuracy across different and diverse styles of datasets by fine-tuning models multiple times, these approaches are time-consuming and hinder model generalization. As such, exploring a training strategy that only requires training once on all datasets is a promising solution. However, the text-style mismatch poses challenges to accuracy in such an approach. To mitigate these issues, we propose a style-aware learning network (SLNText) for style-robust text detection in the wild. This includes a style-aware head to distinguish the text styles of images and a dynamic selection head to realize the detection of images with different text styles. SLNText is only trained once, achieving superior performance by automatically learning from multiple text styles and overcoming the style mismatch issue inherent in one-size-fits-all approaches. By using only one set of network parameters, our method significantly reduces the training consumption while maintaining a satisfactory performance on several styles of datasets. Our extensive experiments demonstrate that SLNText achieves satisfactory performance in several styles of datasets, showcasing its effectiveness and efficiency as a promising solution to style-robust scene text detection.

¿Etnógrafos amateurs? Itinerarios, obras y subjetividades en contacto con la antropología en la Argentina (c. 1920 –1960)

This article problematizes the notion of amateur ethnographers (and ethnography) accordant with the new interest in exploring the margins of institutions of knowledge and the networks between established professionals and other actors linked to Anthropology, especially in Argentina. We compare the trajectories of the Swede Mauricio Jesperson and the German Luis Ruez between 1920 and 1960 and their respective productions on the Pilagá of the Chaco, and about the Rankülche in La Pampa and the Mbyá-guaraní in Misiones. We focus on the multiple meshworks of contact which entwine their practices, works, and subjectivities. Likewise, we analyze the interethnic situation, the circulation and formats of their works, its content and textual style and, last but not least, their relations with institutions, especially with the anthropological academia of the time. We seek to highlight in a historical key how the figure of amateur ethnographers varies, needs to be contextually understood, and brings about new practices and products.

Explicitly-Decoupled Text Transfer With Minimized Background Reconstruction for Scene Text Editing.

Scene text editing aims to replace the source text with the target text while preserving the original background. Its practical applications span various domains, such as data generation and privacy protection, highlighting its increasing importance in recent years. In this study, we propose a novel Scene Text Editing network with Explicitly-decoupled text transfer and Minimized background reconstruction, called STEEM. Unlike existing methods that usually fuse text style, text content, and background, our approach focuses on decoupling text style and content from the background and utilizes the minimized background reconstruction to reduce the impact of text replacement on the background. Specifically, the text-background separation module predicts the text mask of the scene text image, separating the source text from the background. Subsequently, the style-guided text transfer decoding module transfers the geometric and stylistic attributes of the source text to the content text, resulting in the target text. Next, the background and target text are combined to determine the minimal reconstruction area. Finally, the context-focused background reconstruction module is applied to the reconstruction area, producing the editing result. Furthermore, to ensure stable joint optimization of the four modules, a task-adaptive training optimization strategy has been devised. Experimental evaluations conducted on two popular datasets demonstrate the effectiveness of our approach. STEEM outperforms state-of-the-art methods, as evidenced by a reduction in the FID index from 29.48 to 24.67 and an increase in text recognition accuracy from 76.8% to 78.8%.

Authorship style transfer with inverse transfer data augmentation

Authorship style transfer aims to modify the style of neutral text to match the unique speaking or writing style of a particular individual. While Large Language Models (LLMs) present promising solutions, their effectiveness is limited by the small number of in-context learning demonstrations, particularly for authorship styles not frequently seen during pre-training. In response, this paper proposes an inverse transfer data augmentation (ITDA) method, leveraging LLMs to create (neutral text, stylized text) pairs. This method involves removing the existing styles from stylized texts, a process made more feasible due to the prevalence of neutral texts in pre-training. We use this augmented dataset to train a compact model that is efficient for deployment and adept at replicating the targeted style. Our experimental results, conducted across four datasets with distinct authorship styles, establish the effectiveness of ITDA over traditional style transfer methods and forward transfer using GPT-3.5. For further research and application, our dataset and code are openly accessible at https://github.com/AnonymousRole/ITDA.

A Study of Modern Poetry Translation under the Perspective of Discourse Function: T.S. Eliot's The Waste Land in Multiple Translations as an Example

From the discourse function perspective of Halliday's Systemic Functional Linguistics, this paper compares and analyzes two Chinese translations of The Waste Land by Zhao Luorui and Zha Liangzheng (Mudan), exploring how the different translations creatively interpret and reconstruct the original text in accordance with the cultural context and aesthetic preferences of the target language. It further investigates how this reconstruction impacts and shapes the target readers' understanding and acceptance process. The study reveals that the translators' distinct orientations in translation strategies lead to significant differences in the translations regarding expressive effects and readers' experiences. Zhao's translation effectively maintains the discourse structure and linguistic style of the original text through a conservative and traditional translation approach. Conversely, Mudan's translation notably enhances the readability and acceptability of the translation with a flexible and innovative translation style. Overall, the latter translation is better suited to the aesthetic habits and cultural background of Chinese readers.

Practical Reflections on Enhancing Cultural Confidence in English Education in Colleges and Universities under the Big Data Platform

Abstract Day by day, we are realizing the goal of the great rejuvenation of the Chinese nation, which necessitates the development of sufficient cultural self-confidence. The amalgamation of traditional culture and English learning is an essential prerequisite for us to effectively communicate the Chinese story to the world. This paper proposes an optimized ridge regression model with regularization constraints to enhance the preprocessing of cultural data. Subsequently, we propose a statistical model for analyzing the teaching styles of English texts from four perspectives, taking into account the characteristics of English language teaching and the cardinal text statistics algorithm based on word frequency optimization. This model’s analysis revealed that textbook articles reflecting traditional Chinese culture accounted for only 1.8% of all types, resulting in an uneven distribution of grades. Following the implementation of improvements to tackle this issue, 74.86% of the students expressed their belief that enhanced teaching could bolster their cultural confidence. Similarly, 86.6% of the teachers expressed their belief that incorporating Chinese culture into English teaching could enhance students’ cultural awareness, and 90% of the teachers believed it could boost students’ identification with Chinese culture and cultural confidence. It shows that the new teaching method optimized by conducting textual statistical analysis helps to bring into play the role of English education in colleges and universities in enhancing cultural self-confidence.

СТАНОВЛЕНИЕ ЭКОЛОГИЧЕСКОГО ИДЕАЛА В КОНТЕКСТЕ КОНЦЕПЦИИ ИДЕАЛЬНОГО Д. В. ПИВОВАРОВА

The author addresses the urgent problem of the formation and development of a new ecological culture as a strategically important task in the conditions of the ecological crisis. Developing the heuristic potential of the synthetic concept of culture and D. V. Pivovarov’s concept of the ideal, the author analyzes the possibility of understanding the essence of ecological culture based on this concept, the possibility of constructing the ecological culture of the future society based on the concept of the ideal. The key importance of the ideal in the formation of culture is substantiated. The article discusses three models of idealization in the ecological context and the manifestations of these ways of idealization in modern reality. The author analyzes the birth of a new ecological ideal in the format of a specific subjective image of the objective world, embodied in numerous texts of ecophilosophists, which, according to the author, have a limited impact on the broad masses of the population. Тhe author sees the reasons for this in the scientific style of texts and the lack of attractiveness for a modern person of the world order proposed by ecophilosophists. The possibility of the emergence of ecological religion on the principles of metaconfessionality, ecocentrism and biocentrism is interpreted. Modern ecological practices are considered as the second way of idealization, which involves the transformation of activities based on the criterion of long-term, long-term and multidimensional benefits based on the universal value of nature and the remoteness of the consequences of environmental management. To effectively use this path of idealization, support from the state, the media, elites, etc. is needed. The transformation of images-ideals of an ecological nature, embodied, for example, in the change of the aesthetic ideal, is discussed. The article interprets ecotopia as an integral mechanism of idealization, reflecting all the listed models of idealization. As a result of the research, the problems of the correlation of ecological values and ideals with the values of individualism and collectivism are revealed, the need for a new understanding of the idea of the «common good», the search for the ecological meaning of the concept of «quality of life», the study of possible forms of the sacralization of nature in modern conditions is revealed. The results of the research can be used in the implementation of environmental policy to create conditions for the sustainable development of society, in pedagogical design of ways to form environmental values, in ecophilosophical research of the search for strategies for survival and sustainable development.

A Study on the Methods of Improving Writing Skills by Bayesian Classifier in English Teaching and Learning

Abstract Teachers have been researching how to effectively improve the English writing ability of contemporary college students. The article utilizes web crawler technology to establish an English writing knowledge database and preprocesses the data through a word division algorithm, lexical annotation, and word form reduction. The article employs Bayesian classification algorithms to model the English writing style of students. The English writing grammar error correction model was constructed based on the BiGRU model, combined with the BERT pre-training task, fused with the attention mechanism. The English writing knowledge database was used to analyze the data for the model mentioned above. The sentence length of English written text fluctuates between -0.095 and 0.436, and the Bayesian classification model’s AUC value is 0.814. The grammar error correction model for English writing text has a GLEU value of 63.24, with an automatic scoring mean of 26.69, which is only 0.35 higher than manual scoring and a scoring error of only 1.29%. Through the English writing text style and grammar error correction model, teachers can optimize the English writing teaching mode and help students improve their English writing abilities.