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This article presents the results of a study focusing on the reception of a fictional story by Kurt Vonnegut translated from English into Catalan and Dutch in three conditions: machine translated, post-edited, and human translated. Participants (n = 223) rated the three conditions using three scales: narrative engagement, enjoyment, and translation reception. The results show that human translation had higher engagement, enjoyment, and translation reception in Catalan, compared to the post-edited and machine-translated translations. However, Dutch readers scored the post-edited translation higher than the human and machine translation, and the highest engagement and enjoyment scores were reported for the original English version. We hypothesize that when reading a fictional story in translation, not only are the condition and the quality of the translation key to understanding its reception, but also the participants’ reading patterns, reading language, and, potentially, the status of the source language in their own societies.
This article presents the results of a study focusing on the reception of a fictional story by Kurt Vonnegut translated from English into Catalan and Dutch in three conditions: machine translated, post-edited, and human translated. Participants (n = 223) rated the three conditions using three scales: narrative engagement, enjoyment, and translation reception. The results show that human translation had higher engagement, enjoyment, and translation reception in Catalan, compared to the post-edited and machine-translated translations. However, Dutch readers scored the post-edited translation higher than the human and machine translation, and the highest engagement and enjoyment scores were reported for the original English version. We hypothesize that when reading a fictional story in translation, not only are the condition and the quality of the translation key to understanding its reception, but also the participants’ reading patterns, reading language, and, potentially, the status of the source language in their own societies.
Over the last four decades, considerable efforts have been devoted to the modeling and evaluation of human translation processes. This article takes a closer look at the evolution of empirical Translation Process Research (TPR) within the CRITT TPR-DB tradition. It contends that human translation unfolds on various processing levels and puts forth the Free Energy Principle (FEP) and Active Inference (AIF) as a promising framework for modeling these intricately embedded processes in a mathematically rigorous framework. The article introduces innovative methods for quantifying fundamental concepts of Relevance Theory (relevance, s-mode, and i-mode translation) and establishes their connection with the Monitor Model, framing relevance maximization as a special case of free energy minimization. The framework presents exciting prospects for future research in predictive TPR, promising to enhance our understanding of human translation processes and contributing significantly to the broader field of translation studies and cognitive sciences in general.
This study assesses the usability of machine-translated texts in scholarly communication, using self-paced reading experiments with texts from three scientific disciplines, translated from French into English and vice versa. Thirty-two participants, proficient in the target language, participated. This study uses three machine translation engines (DeepL, ModernMT, OpenNMT), which vary in translation quality. The experiments aim to determine the relationship between translation quality and readers’ reception effort, measured by reading times. The results show that for two disciplines, manual and automatic translation quality measures are significant predictors of reading time. For the most technical discipline, this study could not build models that outperformed the baseline models, which only included participant and text ID as random factors. This study acknowledges the need to include reader-specific features, such as prior knowledge, in future research.
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