Emotion Intensity and Context-Aware Word Choice for Translation based on Affective Engineering

Abstract

Although the accuracy and processing capabilities of machine translators have increased greatly in recent years, the question remains as to whether human translators can ever be completely replaced by software without an appropriate model of human feelings. Toward that objective, we propose several different factors that machine translation may use to present results with higher accuracy. The factors are classified as target audience, text genres, context, emotion intensity, and emotion types. We apply three methods in a survey to elicit the respondents’ actual feelings about a given set of translations by varying specific word choices in the translation samples. Analysis of Pearson correlation ratio shows that context and emotion intensity offer potentially useful information for word choice in translation. These results suggest that the result of word choice can be correlated to the proposed factors and varies depending on text genres.