Producing the translators of tomorrow: designing a student-centred and competence-based translation curriculum for Saudi universities
AbstractThe main aim of this research project is to investigate the extent to which translation courses in Saudi Arabia adequately prepare students to take up careers as professional translators according to current market needs. Saudi Vision 2030 acknowledges that graduates must be able to operate at a professional level of competence in order to be competitive in terms of employability. Accordingly, there is a need to improve the translation skills and competences of graduates of translation courses in Saudi Arabia and, more broadly, in the Arabic-speaking world. Using a Saudi case study, this research explores how competency-based course content can be combined with analysis of multiple stakeholders’ perspectives and a review of research, policies, and best practice to identify potential gaps between undergraduate translator training approaches and the needs of the translation industry. Primary data has been collected by surveying four samples: a sample of staff teaching translation modules at Saudi Universities, a sample of students and graduates of EFL and translation at Saudi universities as well as a sample of some of the top employers in Saudi Arabia. The data gathered is intended to help the course designers and educational practitioners in developing translation skills curricula through evidence-based recommendations. By implementing them, universities can more closely align the translation components of undergraduate degree programmes with the needs of the market, thereby enhancing graduates’ employability. The results shed light on the changes that have to be made in the current provision and existing teaching practices, curricula, and student skill sets in Saudi universities. These changes could improve the course design and teaching of translation so that these universities can produce graduates with the necessary vocationally oriented profile to work in the translation sector. This research can also help to inform education policy in the HE sector in Saudi Arabia and the Middle East and North Africa (MENA) region overall.
PublisherUniversity of Wolverhampton
TypeThesis or dissertation
DescriptionA thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.
SponsorsSaudi Arabian Cultural Bureau- University of Jeddah.
The following licence applies to the copyright and re-use of this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International
Showing items related by title, author, creator and subject.
Multiword units in machine translation and translation technologyRuslan, Mitkov; Monti, Johanna; Corpas Pastor, Gloria; Seretan, Violeta (John Benjamins, 2018-07-20)The correct interpretation of Multiword Units (MWUs) is crucial to many applications in Natural Language Processing but is a challenging and complex task. In recent years, the computational treatment of MWUs has received considerable attention but we believe that there is much more to be done before we can claim that NLP and Machine Translation (MT) systems process MWUs successfully. In this chapter, we present a survey of the field with particular reference to Machine Translation and Translation Technology.
Computational Phraseology light: automatic translation of multiword expressions without translation resourcesMitkov, Ruslan (de Gruyter Mouton, 2016-11-01)This paper describes the first phase of a project whose ultimate goal is the implementation of a practical tool to support the work of language learners and translators by automatically identifying multiword expressions (MWEs) and retrieving their translations for any pair of languages. The task of translating multiword expressions is viewed as a two-stage process. The first stage is the extraction of MWEs in each of the languages; the second stage is a matching procedure for the extracted MWEs in each language which proposes the translation equivalents. This project pursues the development of a knowledge-poor approach for any pair of languages which does not depend on translation resources such as dictionaries, translation memories or parallel corpora which can be time consuming to develop or difficult to acquire, being expensive or proprietary. In line with this philosophy, the methodology developed does not rely on any dictionaries or parallel corpora, nor does it use any (bilingual) grammars. The only information comes from comparable corpora, inexpensively compiled. The first proof-of-concept stage of this project covers English and Spanish and focuses on a particular subclass of MWEs: verb-noun expressions (collocations) such as take advantage, make sense, prestar atención and tener derecho. The choice of genre was determined by the fact that newswire is a widespread genre and available in different languages. An additional motivation was the fact that the methodology was developed as language independent with the objective of applying it to and testing it for different languages. The ACCURAT toolkit (Pinnis et al. 2012; Skadina et al. 2012; Su and Babych 2012a) was employed to compile automatically the comparable corpora and documents only above a specific threshold were considered for inclusion. More specifically, only pairs of English and Spanish documents with comparability score (cosine similarity) higher 0.45 were extracted. Statistical association measures were employed to quantify the strength of the relationship between two words and to propose that a combination of a verb and a noun above a specific threshold would be a (candidate for) multiword expression. This study focused on and compared four popular and established measures along with frequency: Log-likelihood ratio, T-Score, Log Dice and Salience. This project follows the distributional similarity premise which stipulates that translation equivalents share common words in their contexts and this applies also to multiword expressions. The Vector Space Model is traditionally used to represent words with their co-occurrences and to measure similarity. The vector representation for any word is constructed from the statistics of the occurrences of that word with other specific/context words in a corpus of texts. In this study, the word2vec method (Mikolov et al. 2013) was employed. Mikolov et al.’s method utilises patterns of word co-occurrences within a small window to predict similarities among words. Evaluation results are reported for both extracting MWEs and their automatic translation. A finding of the evaluation worth mentioning is that the size of the comparable corpora is more important for the performance of automatic translation of MWEs than the similarity between them as long as the comparable corpora used are of minimal similarity.
Comparing post-editing difficulty of different machine translation errors in Spanish and German translations from EnglishZaretskaya, A M; Vela, G; Seghiri, M; Corpas Pastor, Gloria (Center for Promoting Ideas, 2016-08-30)Post-editing (PE) of Machine Translation (MT) is an increasingly popular way to integrate MT in the professional translation workflow, as it increases productivity and income. However, the quality of MT is not always good enough to blindly choose PE over translation from scratch. This article studies the PE of different error types and compares indicators of PE difficulty in English-to-Spanish and English-to-German translations. The results show that the indicators in question 1) do not correlate between each other for all error types, and 2) differ between languages.