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…

Aim/Purpose: Using Artificial Intelligence with Deep Learning (DL) techniques, which mimic the action of the brain, to improve a student's grammar learning process. Finding the subject of a sentence using DL, and learning, by way of this computer field, to analyze human learning processes and mistakes. In addition, showing Artificial Intelligence…

Machine translation has been criticized for its inability to provide language style, but for specialized or technical texts, of which there are increasing numbers, machine translation with its obligatory post-editing may be effective, and the "style" of these translations may be a reflection of the error patterns caught in post-editing. (MSE)

Writing problems common among many college students are "phrasal" errors such as limited vocabulary, inability to distinguish standard usage from slang or jargon, a tendency to frame thoughts in cliches, a peppering of meaningless intensifiers, and a gift for redundancy and wordiness. To help correct these problems, a text-checking system called…

Examined a possible link between computer-generated feedback and changes in writing strategies of English-as-a-foreign-language business-writing students in Taiwan. Numerous detailed analyses were carried out using computer software that measured students' writing, including time spent on a document, amount of editing of a document, specific…