Attention based English-Bodo Neural Machine Translation System for Tourism Domain

Narzary, Sanjib; Brahma, Maharaj; Singha, Bobita; Brahma, Rangjali; Dibragede, Bonali; Barman, Sunita; Nandi, Sukumar; Som, Bidisha

doi:10.1109/iccmc.2019.8819699

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Take Computer-aided Translation (CAT) and Machine Translation (MT) for example, after initial distrust in their affordance to enhance translators’ performance in professional settings ( Wu et al, 2012 ; Koehn, 2015 ; Sam et al, 2015 ; Bundgaard et al, 2016 ; Sun, 2021 ), these technologies have become tools of trade for professionals in recent years ( Olohan, 2011 ; Cadwell et al, 2018 ; Vieira, 2020 ; Vieira et al, 2021 ). After generations of technological upgrades, new technologies have been closely integrated with a variety of translation domains: from audiovisual translation ( Toral et al, 2018 ), clinical medicine ( Khoong and Rodriguez, 2022 ), business activities ( Bowker, 2020 ) to tourism ( Narzary et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Teaching data science to undergraduate translation trainees: Pilot evaluation of a task-based course

Yan

2022

Front. Psychol.

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The advancement in technology has changed the workflow and the role of human translator in recent years. The impact from the trend of technology-mediated translation prompted the ratification of technology literacy as a major competence for modern translators. Consequently, teaching of translation technology including but not limited to Computer-aided Translation (CAT) and Machine Translation (MT) became part of comprehensive curricula for translation training programs. However, in many institutions, the teaching of translation technology was haunted by issues such as: narrow scope of curriculum design, outdated technologies, and unbalance between theories and practices in teaching. The study was the pilot evaluation of a tailored course to foster translation trainees’ knowledge and abilities of data science. The course was designed to be a fundamental step toward sophisticated translation technologies. During the pilot evaluation of the 8-week course, 85 students (n = 85) were recruited as participants. The study adopted a mix-method design by employing a survey to investigate student’s level of satisfaction toward the course and focus group discussion to understand students’ attitudes and perceptions of key aspects of the course. By interpreting the results from statistical analysis of the survey (5.39/7) and thematic analysis of the focus group discussion, the course of data science for translators was well received among participants. The evaluation project manifested the feasibility and effectiveness of a translator-oriented data science course.

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