This dissertation developed a new learner corpus of Japanese and introduced an error and linguistic annotation scheme specifically designed for Japanese particles. The corpus contains texts written by learners who are in the first year to fourth year university level Japanese courses. The texts in the corpus were tagged with part-of-speech and…

Considering noticeable improvements in the accuracy of Google Translate recently, the aim of this study was to examine second language (L2) learners' ability to use post-editing (PE) strategies when applying AI tools such as the neural machine translator (MT) to solve their lexical and grammatical problems during L2 writing. This study examined 57…

In this study, we examined whether interpreters and interpreting trainees are better predisposed to respeaking than people with no interpreting skills. We tested 57 participants (22 interpreters, 23 translators and 12 controls) while respeaking 5-minute videos with two parameters: speech rate (fast/slow) and number of speakers (one/many). Having…

This research work aimed at making use of Machine Translation to help students avoid some syntactic, semantic and pragmatic common errors in translation from English into Arabic. Participants were a hundred and five freshmen who studied the "Translation Common Errors Remedial Program" prepared by the researchers. A testing kit that…

Two experiments and two corpus studies focus on homophone dominance in the spelling of regularly inflected verb forms, the phenomenon that the higher-frequency homophone causes more intrusion errors on the lower-frequency one than vice versa. Experiment 1 was a speeded dictation task focusing on the Dutch imperative, a verb form whose formation…