Machine translation (MT) is the automated process of translating text between different languages, encompassing a wide range of language pairs. This study focuses on non-professional bilingual speakers of Turkish and English, aiming to assess their ability to discern accuracy in machine translations and their preferences regarding MT. A particular…

Neural Machine Translation (NMT) has gained increasing popularity among EFL learners as a CALL tool to improve vocabulary, and many learners have reported its helpfulness for vocabulary learning. However, while there has been some evidence suggesting NMT's facilitative role in improving learners' writing on the lexical level, no study has examined…

The present study attempted to examine the effect of using the connectivist approach on developing secondary-stage students' cross-cultural awareness and translation performance. The study comprised thirty-two first-year secondary stage students enrolled in El-Jalawea Institute, Sohag Governorate. The study adopted the quasi-experimental design.…

The use of machine translation (MT) tools remains controversial among language instructors, with limited integration into classroom practices. While much of the existing research into MT and language education has explored instructor perceptions, less is known about how students actually use MT or how student use compares to instructor beliefs and…

Multilingualism presents both a challenge and an opportunity for Natural Language Processing, with code-switching representing a particularly interesting problem for computational models trained on monolingual datasets. In this paper, we explore how code-switched data affects the task of Machine Translation, a task which only recently has started…

This study aimed to examine the effects of an AI-based object detection translation (AI-based ODT) application (app) on EFL students' vocabulary learning. We developed a system that utilized strategies to facilitate learners' vocabulary learning. The app applied dual code theory to present the objects in picture, word, and pronunciation formats.…

In recent years, machine translation post-editing (MTPE or PE for short) has been steadily gaining ground in the language industry. However, studies that examine translators' perceptions of, and attitudes towards, MTPE paint a somewhat negative picture, with PE pricing methods and rates being a major source of dissatisfaction. While the European…

Mass adoption of neural machine translation (NMT) tools in the translation workflow has exerted a significant impact on the language services industry over the last decade. There are claims that with the advent of NMT, automated translation has reached human parity for translating news (see, e.g. Popel et al. 2020). Moreover, some machine…

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…

This study seeks to contribute to addressing a gap in theory-driven corpus-based research focused on the so-called translation specific features (TSF) in Arabic translated texts. It provides a contrastive Systemic Functional Linguistics (SFL)-informed analysis of concessive/contrastive connective markers in a selected comparable corpus made up of…

This study employed speech-enabled language translation (SELT) technology in lectures presented in English as a medium of instruction (EMI). Previous research had students viewing pre-recorded lectures with incorporated content translations in controlled laboratory settings. In contrast, students in the present study experienced live lectures in…

The use of machine translation (MT) in the academic context has increased in recent years. Hence, language teachers have found it difficult to ignore MT, which has led to some concerns. Among the concerns, its accuracy has become a major factor that shapes language teachers' pedagogical decision to use MT in their language classrooms. Despite the…

In recent years, an array of studies has focused on 'translationese' (i.e., unique features that manifest in translated texts, causing second language (L2) writings to be similar to translated texts but different from native language (L1) writings). This intriguing linguistic pattern has motivated scholars to investigate potential markers for…

In a global context increasingly prone to e-learning formats at all levels of education, especially in and after the context of the COVID-19 pandemic, the acquisition of skills in distance learning programmes should not differ from the ones in onsite learning programmes. The main objective of this study is to analyse the perception that master's…

University students, especially language learners, have increasingly been using machine translation (MT) systems in the last decade and for all kinds of texts, including homework, assignments and exams. This ubiquity does not translate into visibility as few teachers address the subject in class. Several researchers have shown that MT systems,…