Algorithmic textual practices: Improving fluency and word order in neural machine translation output

Abstract

This research investigates how to improve fluency and word order in neural machine translation output. Building my argument by drawing on data collected from the Boston Consulting Group, Deloitte, eMarketer, Locaria, MIT Sloan Management Review, NCSC, and Statista, I performed analyses and made estimates regarding awareness and usage of translation applications featuring machine learning (%), how professional translators worldwide see artificial intelligence affecting their work in the future (%), and market size of the global language services industry (billion U.S. dollars). The results of a study based on data gathered from 4,200 respondents provide support for my research model. Employing the structural equation modeling and using the probability sampling technique, I collected and inspected data via a self-administrated questionnaire.