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…

We investigated the perception and recognition of errors in a population of practitioners and academics in professional and technical communication. Specifically, we measured 303 participants' botheration levels of 24 usage errors and then correlated those results against their ability to recognize the errors. Results indicated that practitioners…

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…

Generalised access to the Internet and globalisation has led to increased demand for translation services and a resurgence in the use of machine translation (MT) systems. MT post-editing or the correction of MT output to an acceptable standard is known to be one of the ways to face the huge demand on multilingual communication. Given that the use…

The use of error pattern analysis can reduce the time and money spent on editing and correcting manuscripts. What is required is noting, classifying, and keeping a frequency count of errors. First an editor should take a typical page of writing and circle each error. After the editor has done a sufficiently large number of pages to identify an…

Error pattern analysis is a teaching technique that emphasizes identifying, classifying, and keeping a frequency count on only those errors actually occurring in students' writing. Application of error pattern analysis in a workshop format requires three steps: preparing an error pattern analysis, teaching from this analysis, and integrating it…

Examines the use of computers for error detection in natural language texts. Focuses on a computer program designed to teach students to edit their papers for errors using pattern matching in error detection. Describes a "pedagogy for editing" and speculates on ways to improve computer detection of errors in natural language texts. (AEM)