Limitations of Machine Translation: Focused on the Role of Cognitive Complements in Human Communication
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Significant progress has been made in machine translation since the introduction of the Neural-based Machine Translation (NMT) algorithms. For Korean-English translation, there is growing evidence that the machine translation engines are rendering more and more acceptable translations with time through deep learning. The progress inevitably raises the question of whether human expertise will eventually be replaced by machines. This question depends on whether and how much of the inter-lingual mediation process depends on inherently human capabilities. To understand the potential boundaries of machines translation, this study draws on the role of cognitive complements in face-to-face communication, focusing on spoken as opposed to the commonly researched written texts. The first part of the study discusses the role of cognitive complements in human communication. Then to illustrate the important role played by cognitive complements in human communication, the second part of the study explains the errors made by NMT from the perspective of cognitive complements, based on a sample text analysis using Google Translate. The analysis indicate that applying the current engine to face-to-face encounters has serious pragmatic risks. From the perspective of machine translation, therefore, the ability to apply cognitive complements like human beings, with the ability to draw on extra-linguistic context may be the key to making NMT ready for real-world applications as far as the processing of spoken texts is concerned. While effort is underway to relate sensory experience to text processing, enormous computing power requirement may be another obstacle before human interpreters can be replaced completely. Pedagogic implications of this study for post-graduate interpreting programs are discussed in the final part of the paper.
Abstract
Significant progress has been made in machine translation since the introduction of the Neural-based Machine Translation (NMT) algorithms. For Korean-English translation, there is growing evidence that the machine translation engines are rendering more and more acceptable translations with time through deep learning. The progress inevitably raises the question of whether human expertise will eventually be replaced by machines. This question depends on whether and how much of the inter-lingual mediation process depends on inherently human capabilities. To understand the potential boundaries of machines translation, this study draws on the role of cognitive complements in face-to-face communication, focusing on spoken as opposed to the commonly researched written texts. The first part of the study discusses the role of cognitive complements in human communication. Then to illustrate the important role played by cognitive complements in human communication, the second part of the study explains the errors made by NMT from the perspective of cognitive complements, based on a sample text analysis using Google Translate. The analysis indicate that applying the current engine to face-to-face encounters has serious pragmatic risks. From the perspective of machine translation, therefore, the ability to apply cognitive complements like human beings, with the ability to draw on extra-linguistic context may be the key to making NMT ready for real-world applications as far as the processing of spoken texts is concerned. While effort is underway to relate sensory experience to text processing, enormous computing power requirement may be another obstacle before human interpreters can be replaced completely. Pedagogic implications of this study for post-graduate interpreting programs are discussed in the final part of the paper.
참고문헌 (29)
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