The aim of this article is to investigate the morphological and lexicosemantic traits of the COVID-19 lexeme as demonstrated in the framework of the English Web 2021 (enTenTen21) and the Slovak National Corpus (web-6.0). The exploration of the word-formation potential of the lexeme is carried out, too. A preliminary step of the research procedure is to recognize recurring patterns in which the studied lexical unit occurs so that we could inquiry into its functions in the given discourse. To achieve its goals, the combination of the lexical analysis and corpus analysis methods is employed. The findings of this study reveal that the lexeme COVID-19 entered all spheres of communication within both the studied languages. Furthermore, it has been adapted to their morphological and lexical systems and can access regular word-formation processes existing in them.
Pronunciation remains a controversial aspect of English instruction, both in theory and practice, with the dichotomy between “nativeness” and “intelligibility” (Levis, 2005) directing research into the exploration of learner and teacher views as to what they want to achieve, and, consequently, which elements of the English sound system should be included in language teaching and learning. Continuing a long tradition of questionnaire studies among Polish learners and teachers of English, this paper explores the presence of pronunciation instruction in the English classroom by inviting students who have chosen English as their BA major to reflect on their experience. The total of 70 students participated in a questionnaire study in which they were asked to reflect on whether they remembered pronunciation to have been included in their English instruction at different levels of schooling, and if it was, how it was taught and which aspects they remembered to have been practiced. They were also encouraged to provide comments and to share their views on the usefulness of instruction as well as to provide recommendations for pronunciation teaching and learning. The results show that pronunciation is only marginally present in students’ learning experience, with a slight increase corresponding to the level of schooling and a growing preference for suprasegmental phonetics. While commenting on their experience, students stress the need for pronunciation practice, correction by the teacher and self-study to be included from the beginning of EFL education.
This paper draws heavily from my previous work on intelligibility (Hodgetts, 2020). It advocates basing pronunciation instruction on intelligibility goals, rather than native-like production goals and investigates the research available on the segmental and suprasegmental features that should be prioritized in order to enhance intelligibility and comprehensibility. First, the Chapter defines and explains the concepts of intelligibility, comprehensibility and accentedness, before discussing the merits of native and intelligibility-based targets of instruction in various contexts. It then examines which elements of segmental and suprasegmental language instruction might be included in an intelligibility-based syllabus. The crucial role of the listener is explored, as is the issue of English used as a lingua franca.
Microcredentials have recently attracted substantial attention in academia. While short, practice-oriented courses and the concept of lifelong learning (e.g. Jarvis 2009) have been around for decades, the idea of receiving credit in the form of open digital badges and stacking them to form a personal portfolio is new – and exciting. Add to it the ongoing discussion on the decline of the university diploma as such, and a viable alternative to traditional translator education looms in the distance. This paper explores the rise of microcredentials in general and in language and translation studies in particular. The numerous approaches to translation competence (see Quinci 2023 for a more recent one) seem to be in agreement that it is a construct made up of several subcompetencies. It is logically sound, therefore, to acquire these subcompetencies separately in the process of competence development that is individual and free from the typical constraints of university education, such as completing it in the allotted time. However, this system means that there is no authority to tell the trainee that they are „competent enough”, other than perhaps the employer, who chooses to hire them or not. As regards the translation market, microcredentials are too new to be universally recognised, so for the time being translation agencies will prefer more traditional qualifications. This, in turn, leads to higher education institutions shying away from offering them (and students from taking them), since they do not yet constitute a fully-fledged qualification. The paper includes a presentation of a pilot microcredential on offer at University of Lodz, as well as the results of a focus group survey on microcredentials in translation.
Mental health is increasingly recognized as a critical issue in interpreter training. Interpreters frequently encounter cognitive and emotional stressors (Moser–Mercer et al., 1998; Valero-Garcés, 2005). Despite growing awareness, interpreter education often lacks structured support for emotional regulation and stress management – skills essential for maintaining performance and well-being. In Slovakia, the mental health of interpreters gained attention following the Ukrainian refugee crisis, which saw many interpreting students thrust into crisis situations with little preparation (Hodáková and Ukušová, 2023). This chapter, based on survey data from 116 students and 12 interpreting teachers across Slovak universities, highlights the urgent need to integrate resilience and personality-focused training into interpreter education. Recommendations include expanding Nitra’s 'Mental Hygiene' course and embedding personality-based activities into practical interpreting training, especially for future public service interpreters.
This paper is aimed at exploring some traits of neo-standard Italian in four different spoken corpora, such as children’s conversations, adult conversations, parliamentary debates, and film subtitles. The following features are investigated: multifunctional che, cleft sentences, and presentational c’è. Building on scholarly examples and sample phrases containing the traits above mentioned, this paper discusses how to search for, retrieve and analyse neo-standard phrasemes in the four corpora. Other features are also presented and investigated on a case-by-case basis. In addition, relative frequencies of phraseme occurrences are noticed and discussed. In this way, a qualitative and a small-scale quantitative comparative approach is developed. The paper findings report that all the traits appear in the corpora with some notable genre-related differences.
Remote interpreting experienced rapid expansion during the COVID-19 pandemic. Although there are no pandemic restrictions today in Slovakia, the interpreting market has not returned to its pre-pandemic state. Presented chapter examines how Slovak universities prepared future interpreters during online education, especially in regard to remote interpreting. Focus group and individual interviews were conducted with 15 interpreting teachers from all Slovak universities that are offering translation and interpreting program. Analysis revealed a lack of systematic integration of remote interpreting in the university training before and during the pandemic; problems with finding a suitable videoconferencing platform for the training; both good and bad practices. Exploring remote interpreting during the pandemic era helps understand the current practices in the training and the market.
This study examines the distribution and characteristics of three linguistic structures across three different semantic types of adjectives in English: opinion-based (evaluative), mixed (dimensional), and non-opinion (objective) adjectives. While previous research has explored the perception of subjective adjectives in varied linguistic environments through experimental methods, our research provides quantitative corpus-based evidence from Universal Dependencies English corpora. The analysis focuses on three syntactic structures: modification of a noun (Adj+Noun), predicative construction with nexus (Nexus+Cop+Adj), and predicative construction with noun subject (Noun+Cop+Adj). Results reveal significant patterns: opinion adjectives display greater structural flexibility, appearing with meaningful frequency in both attributive and predicative positions, while dimensional adjectives strongly favor attributive position. The study also identifies consistent positional patterns across all adjective types, with attributive structures typically appearing later in sentences than predicative constructions. Furthermore, register variations were observed, with web language showing distinctive distributional patterns for opinion adjectives compared to more varied texts.
Women entrepreneurs’ blogs represent a unique and evolving form of digital discourse, blending professional authority with personal engagement. Within this genre, evaluative language plays a crucial role in shaping credibility, persuasion, and identity. While evaluative adjectives have been widely studied in formal and academic contexts, their opinion-forming function in entrepreneurial communication remains largely unexplored. This study investigates how opinions and evaluations are constructed through the use of evaluative adjectives in women entrepreneurs’ blogs, examining their distribution, rhetorical function, and impact on audience engagement. Using a corpus-assisted methodology, the analysis is conducted on the lexical data extracted from the Women Entrepreneurs Blog Corpus (WEBC), a dataset of 329,896 words from 318 blog posts. The study identifies evaluative adjectives through frequency-based corpus analysis and categorises them into distinct semantic groups, which serve as the foundation for examining how women entrepreneurs use linguistic choices to construct stance, authority, and persuasion in digital business communication. A quantitative analysis of categorised evaluative adjectives reveals that positive-polarity evaluative adjectives are the most frequent, reinforcing optimism and motivation. Adjectives of importance follow, emphasising authority and expertise, while size- and time-related adjectives occur moderately, highlighting growth and progress. Attitude and emotion adjectives appear less frequently, contributing to a confident and engaging tone. Negatively-charged adjectives are rare and often reframed, whereas certainty and likelihood adjectives are the least frequent, reflecting a preference for flexibility over absolutes in entrepreneurial discourse. By situating this analysis within the broader framework of stance and evaluation in specialised discourse, this study provides insights into how women entrepreneurs use language to formulate and express opinions, navigate professional identity, establish credibility, and engage their audiences. The research contributes to discussions on opinion expression in digital business communication, shedding light on the intersection of gender, entrepreneurship, and linguistic strategies in online discourse.
Translation has been fundamentally reshaped by artificial intelligence (AI) technology, which increasingly dictates workflows that boost human efficiency but diminish human agency. While these tools may indeed enhance efficiency, they simultaneously erode the cognitive and reflective dimensions of translator practice and education. This article highlights personal resources as essential for sustaining judgment and professional autonomy. AI-assisted tools have indeed transformed the ways translators work and learn, but they cannot replace the uniquely human capacity for purposeful self-development, which is the central focus of this article. The article emphasises how fostering personal resources in translator education can empower future professionals to not only adapt to technological change but to shape it consciously and ethically. The underlying assumption is idealistic, perhaps, yet essential for preserving translation quality and the evolving identity of translators. Translator education should nurture individuals who are not only competent but also human, humane and fully engaged in their professional and personal growth.
Despite increased academic interest in the factors related to individual differences in second language (L2) pronunciation, little is known about how motivated learners utilise pronunciation learning strategies (PLS) to master the target language sound system. Grounded in the self-regulated learning model, this study examined the interplay between PLS and motivation in shaping L2 comprehensibility among 103 learners in Japan. The participants completed a questionnaire that assessed their individual differences in PLS and motivation and undertook a test that measured their L2 comprehensibility. An exploratory factor analysis revealed a two-factor model consisting of PLS and motivation, which was further analysed using Pearson correlation and structural equation modelling. The analysis showed a statistically significant correlation between PLS and L2 comprehensibility, and between motivation and PLS, whereas the correlation between motivation and L2 comprehensibility was not statistically significant. The structural analysis revealed that motivation exerted a significant effect on L2 comprehensibility through PLS, with PLS being a predictor of L2 comprehensibility. Highlighting the often neglected importance of PLS, the findings suggest that the participants were motivated and goal-oriented, which led to their use of PLS. This implies that PLS use and motivation were significant factors in improving L2 comprehensibility in the classroom.
Modern information technologies enable the automatic analysis of textual data to detect extremist and propagandistic content. This paper examines deep learning methods and transformers models for the automatic classification of ideologically charged texts in the Kazakh language. A comparison was conducted between neural network models (CNN, BiLSTM, GRU, Hybrid CNN+BiLSTM) and modern transformers (DistilBERT). The performance evaluation of the models was based on accuracy, recall, precision, and F1-score metrics, as well as error analysis. Experimental results showed that hybrid CNN+BiLSTM demonstrated the highest accuracy (95.11%), outperforming other models. CNN, BiLSTM and GRU also achieved high results (92-93%), making them effective for this task. Among transformers, DistilBERT proved to be the most balanced (85.74%). This study demonstrates that hybrid neural network models (CNN+BiLSTM) are the most effective solution, while DistilBERT performs best among transformer models. The findings can be utilized for developing automatic monitoring and filtering systems for Kazakh-language texts, capable of efficiently identifying ideologically charged content.
This paper examines the under-researched field of Polish–English telephone interpreting, with particular focus on the interactional environment and methods of connection. It first outlines the emergence of telephone interpreting, considers its advantages and disadvantages, and then presents a summary of findings from a corpus study conducted in 2024, in which 250 Polish–English interpreting interactions were recorded and analysed with reference to the method of connection between parties and the sectoral distribution of topics or issues discussed in those interactions. As OPI is conducted exclusively through the auditory channel, interpreters are deprived of visual cues; consequently, each method of connection presents both benefits and drawbacks, with the three-way conference call emerging as the most effective format for telephonic interactions between Polish speakers who decide to use the services of Polish-English OPI interpreters.
The purpose of this study is to examine what influence the machine translation tools using the elements of Artificial Intelligence (AI) have on students’ translation training. In the theoretical part such notions as machine translation, AI and machine translation education are presented according to Fengqi, Yuxuan (2025), Kruk, Kałużna (2024), Chiu et al. (2023), Bahdanau et al. (2016). The analysis is based on the translation project in which students are asked to compare the applicability of the selected machine translation tools using AI with those machine translation tools that are not equipped with AI. Additionally, students’ interviews are examined as to their opinions about AI machine translation tools in students’ translation training. The findings are scrutinized quantitatively and qualitatively. Finally, conclusions are drawn.
The paper presents the generation process and analysis of Chat GPT-produced terminology, definitions, translation and ontology creation of content derived from a restricted domain of electrotechnology on the basis of German manufacturing instruction (Fertigungsvorschrift) for the assembly and functioning of thermal switches. The first part of the study presents a brief historical sketch of corpus linguistics towards the development of LLMs and their applications. Research steps include the generation of terminology and a basic restricted domain ontology in German and their equivalents in English and Polish as identified in general web resources, followed by the same tasks based on the analysed tool instructions. The tests were first performed on earlier ChatGPTPro, followed by its recent version ChatGPT4, also with regard to their English and Polish equivalents, for different tasks, i.a., relevant thesauri building. An Assistant called SLA (Special Language Assistant) was created for the purpose of analysing prompts, recognising context and intent, and processing data using language models. The tests have been carried out in terms of 4 prompts. First, specialist terms typical of the product and its parts were identified in German and the translation of these terms and of other relevant specialist phrases into English and Polish was performed. Finally, the ontological categorisation and its visualisation were generated. Results indicate areas of fair correspondences with manual intervention needed for the term definition refinement and ontology specification. The presentation emphasizes in the conclusions the effects of AI tasks as a contribution to lexicography, translation and foreign language education. The study can also serve as a reference for NLP researchers to improve the functioning of LLM tools.
The present study is based on the interrelation of translation studies, pedagogy and technology in order to develop students’ textual competence by identifying efficient teaching practices in translator training. Textual competence involves also the understanding and application of various sign systems in contemporary digital texts for meaning representation, thus underlying the transdisciplinary status of Translation Studies as its scope of research has been expanded to cognitive science, neurolinguistics, psycholinguistics, information and communication technologies, computational linguistics, and information processing. The constant search for novel approaches to text analysis and the continuous improvement of the existing teaching strategies are necessary to develop students’ ability to effectively decode and convey meaning within a required context. Nowadays development of textual competence is mostly based on the improvement of such skills of students as professional competence and special text analysis to be able to decode meaning at different levels. The authors present a mosaic of teaching activities that are proposed taking into account skills and abilities that translation students need to develop from the perspective of continuously changing modes of communication. The research findings can serve as the source of inspiration for developing study modules within such study courses as Text Analysis Methods, Stylistics and Editing in the framework of the theory of translation.
Modern text data processing and classification methods require extensive use of machine learning and neural networks. Categorizing text into different classes has become a crucial task in many fields. This paper presents a multi-class text classification model utilizing a Modified TF-IDF (MTF-IDF) approach in combination with Long Short-Term Memory (LSTM) neural networks, XGBoost, and MLPClassifier algorithms. Additionally, the study explores the integration of TF-IDF and CountVectorizer (MTF-IDF) methods for text vectorization, aiming to enhance classification efficiency. The research findings indicate that the LSTM model achieved the highest accuracy rate of 89%, demonstrating superior performance. The MLPClassifier model achieved 85% accuracy, while XGBoost obtained 81% accuracy. Moreover, the integration of TF-IDF and MTF-IDF methods significantly improved the detection of rare but essential words, enhancing the overall performance of the models. This study is dedicated to addressing the problem of automated detection of harmful content in the Kazakh language. Hate speech in the digital space refers to any online material that harms individuals or communities through aggression, manipulation, discrimination, or the intentional spread of socially damaging narratives. The results provide a solid foundation for future research aimed at the early identification and mitigation of hate speech in the digital space, contributing to a safer online environment.
The paper presents a method of online collection of linguistic data from informants, developed in a cross-linguistic project on verbal valency in the languages of Europe. The method is essentially an online questionnaire. The rules of the creation of its contents as well as specific problems related to data provision and data annotation are discussed in detail. The proposed method ensures the appropriate level of detail needed, enables the collection of a database sizeable enough and secures the comparability of the data.
This paper presents qualitative results on attitudes to English as an International Language (EIL) at a multicultural, highly diversified community of United World College East Africa, Tanzania, Moshi campus. The results reveal that nearly half of the respondents admit to preferring one native variety of English, with most of the outgroup participants expressing the wish to speak with a British or American accent. The most common ways of adjusting speaking for the benefit of communicative partners are paraphrasing, repetition and slowing down. Vietnamese, Asian English in general, and Irish turn out to be the most difficult accents to comprehend. Pronunciation is the most frequent reason for incomprehensibility. Among the ways of adjusting to accents, listening carefully and immersion are the most often used.
