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Dongkawang Shin; Yuah V. Chon – Language Learning & Technology, 2023
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…
Descriptors: Second Language Learning, Second Language Instruction, Translation, Computer Software
What to Expect from Neural Machine Translation: A Practical In-Class Translation Evaluation Exercise
Moorkens, Joss – Interpreter and Translator Trainer, 2018
Machine translation is currently undergoing a paradigm shift from statistical to neural network models. Neural machine translation (NMT) is difficult to conceptualise for translation students, especially without context. This article describes a short in-class evaluation exercise to compare statistical and neural MT, including details of student…
Descriptors: Translation, Teaching Methods, Computational Linguistics, Quality Assurance
Peer reviewedFeuerman, Ken; And Others – CALICO Journal, 1987
Discusses the theoretical basis, implementation, and pedagogical considerations of CALLE (Computer-Aided Language Learning Environment), a dialogue-based beginning Spanish language instruction system. CALLE uses Lexical Functional Grammar Theory to analyze errors in student input. Sample screen is shown. (Author/LMO)
Descriptors: Adult Learning, Artificial Intelligence, Computational Linguistics, Computer Assisted Instruction

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