1. Translation of Situation Awareness Rating Technique Questionnaire in SlovenianKristina Stojmenova Pečečnik, Grega Jakus, 2025, independent scientific component part or a chapter in a monograph Abstract: The Situational Awareness Rating Technique (SART) is a questionnaire designed to assess situational awareness when operating a machine. It consists of ten questions corresponding to ten dimensions that can be used to assess an operatorǯs situational awareness when interacting with and operating a machine. Although initially developed for assessment situational awareness of pilots, with the rising complexity of vehicles and introduction of automation, its use has spread significantly in automotive domain in the past decade. In this paper, we present the Slovenian version of the original questionnaire and the process of its development. For the translation of the questionnaire SART into Slovenian, the standard procedures for the translation of research instruments were applied: translation, coordination, back-translation into the source language, comparison of the back-translation with the source text and final coordination. Lastly, it was validated against a reference system in a driving-based environment.
Keywords: situational awareness rating technique, situational awareness, in-vehicle human-machine interaction, questionnaire, translation
Published in RUP: 28.01.2026; Views: 1177; Downloads: 2
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5. Parse tree based machine translation for less-used languagesJernej Vičič, Andrej Brodnik, 2008, original scientific article Abstract: The article describes a method that enhances translation performance of language pairs with a less used source language and a widely used target language. We propose a method that enables the use of parse tree based statistical translation algorithms for language pairs with a less used source language and a widely used target language. Automatic part of speech (POS) tagging algorithms have become accurate to the extent of efficient use in many tasks. Most of these methods are quite easily implementable in most world languages. The method is divided in two parts; the first part constructs alignments between POS tags of source sentences and induced parse trees of target language. The second part searches through trained data and selects the best candidates for target sentences, the translations. The method was not fully implemented due to time constraints; the training part was implemented and incorporated into a functional translation system; the inclusion of a word alignment model into the translation part was not implemented. The empirical evaluation addressing the quality of trained data was carried out on a full implementation of the presented training algorithms and the results confirm the employability of the method.
Keywords: machine translation, parse tree
Published in RUP: 15.10.2013; Views: 14100; Downloads: 167
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