Training translators on open-source MT technology : an empirical assessment of learning using a task-based syllabus
This research project tests whether an intensive course based on a newly-formulated task-based (TB) syllabus can equip Master's trainee translators with the essential skills to create their own machine translation (MT) systems. The focus is on under-resourced language pairs, and for non-European teaching settings, therefore, the syllabus was tested on two groups of Master's level English-Arabic translation students in two Arabic-speaking countries, Oman and Jordan. These two groups who joined the training course had no previous knowledge of MT. The free and open-source statistical machine translation (SMT) software, Moses, was used as a training platform. Using a form of Action Research, the learners' engagement was monitored by collecting data on their experience and their reactions to the pace of delivery and the content of the syllabus, whilst assessing their achievements in using the SMT software. For the data collection, a multi-method approach was adopted, including questionnaires, student learning logs, TB assessment, interviews, focus groups, tutor's log and classroom observations. The findings have shown that the proposed TB syllabus is a suitable introduction to enable translation students, with
Al Sharou, Khetam Y.
Training translators on open-source MT technology : an empirical assessment of learning using a task-based syllabus. This research project tests whether an intensive course based on a newly-formulated task-based (TB) syllabus can equip Master's trainee translators with the essential skills to create their own machine translation (MT) systems. The focus is on under-resourced language pairs, and for non-European teaching settings, therefore, the syllabus was tested on two groups of Master's level English-Arabic translation students in two Arabic-speaking countries, Oman and Jordan. These two groups who joined the training course had no previous knowledge of MT. The free and open-source statistical machine translation (SMT) software, Moses, was used as a training platform. Using a form of Action Research, the learners' engagement was monitored by collecting data on their experience and their reactions to the pace of delivery and the content of the syllabus, whilst assessing their achievements in using the SMT software. For the data collection, a multi-method approach was adopted, including questionnaires, student learning logs, TB assessment, interviews, focus groups, tutor's log and classroom observations. The findings have shown that the proposed TB syllabus is a suitable introduction to enable translation students, with
training translator open_source technology empirical assessment learn use task_based syllabus project test_whether intensive course base newly task_based syllabus equip master trainee_translator essential skill create machine_translation system focus under language_pair non_european teaching setting therefore syllabus test two_group master level english_arabic translation student arabic_speaking country oman jordan two_group join train course previous knowledge free open_source statistical_machine translation_smt software moses use training platform using form action learner engagement monitor collect_data experience reaction pace delivery content syllabus whilst assess achievement use smt software data_collection multi_method adopt include questionnaire student_learn log assessment interview focus group tutor log classroom_observation finding_show proposed syllabus suitable introduction enable translation student