Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

Results found: 4

first rewind previous Page / 1 next fast forward last

Search results

Search:
in the keywords:  ENGLISH LANGUAGE
help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
EN
Students in Western university contexts require multiple literacies, numeracies, and critical capacities to succeed. Participation requires a blend of English language capacity, cultural knowhow, and cognisance of the often-hidden racialized assumptions and dispositions underpinning literate performance. Students from Culturally and Linguistically Diverse (CALD) backgrounds transitioning to Western university settings from local and international contexts often find themselves floundering in this complex sociocultural web. Many students struggle with the English language preferences of their institutions despite meeting International English Language Testing System (IELTS) requirements. Once enrolled, students from CALD backgrounds need to navigate the linguistic, semiotic, and cultural landscape of the university, both physically and virtually, to enter the discourses and practices of their chosen disciplines. Universities cannot afford to allow students to ‘sink or swim’ or struggle through with non-specialist or ad-hoc support. In response to a clear need for explicit and ongoing English language support for students from CALD backgrounds, the Student Learning Centre (SLC) at Flinders University in South Australia created the English Language Support Program (ELSP). The ELSP sets out to overcome prescriptive and assimilationist approaches to language support by adopting an eclectic blend of learner-centred, critical-creative, and multi-literacies approaches to learning and teaching. Rather than concentrate on skills and/or language appropriateness, the ELSP broadens its reach by unpacking the mechanics and machinations of university study through an intensive—and transgressive—multi-module program. This paper outlines the theoretical and pedagogical challenges of implementing the ELSP.
EN
Machine translation is currently a very widespread translation technology, translating from one language to another. In the first part of the paper, we explain the basic principles of the machine translation process. We also identify the reasons behind their error rate, and the influence of grammatical differences of the translated languages and type of translated texts on the error rate. In the second, exemplification part of the paper, we categorize and analyse errors that have occurred in the machine translation of technical documentation from English to Slovak. We believe that our analysis will prove the perspective and usability of machine translation, especially in texts that are not difficult to translate (texts with stereotyped expressions using schematic constructions, sentences, such as technical documentation), despite the errors which occurred in translations (the analysis of the machine translation output showed that not every error is equally serious with regard to its influence on the adequate transfer of the meaning of the source text into the target text).
EN
The article considers the nature of descriptive statements and the ontological status of descriptive constructs in linguistics, taking the example of a phoneme of English. It is argued that descriptive statements should be seen as expressions of the content of descriptive models or as hypotheses. Furthermore, it is argued that descriptive models and constructs in linguistics have a purely explanatory function in relation to speech events and without ontological commitment to corresponding entities in the real world.
EN
In this paper, we deal with the issues of machine translation based on neural network. We explain the fundamental principles of this approach to automatic translation. Based on the analysis of statistical and neural machine translations from English into Slovak, we compare the quality, and/or the qualitative shifts after changing the approach to machine translation, from statistical to neural networks. The results reveal that neural machine translation achieves better results in fluency and grammatical correctness of translation, but the representation of semantically inadequate translations increases in examined corpus of journalistic texts.
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.