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Your search keyword '"Hodzik, Ena"' showing total 8 results
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8 results on '"Hodzik, Ena"'

1. Incorporating Human Translator Style into English-Turkish Literary Machine Translation

Author

Yirmibeşoğlu, Zeynep, Dursun, Olgun, Dallı, Harun, Şahin, Mehmet, Hodzik, Ena, Gürses, Sabri, and Güngör, Tunga

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Although machine translation systems are mostly designed to serve in the general domain, there is a growing tendency to adapt these systems to other domains like literary translation. In this paper, we focus on English-Turkish literary translation and develop machine translation models that take into account the stylistic features of translators. We fine-tune a pre-trained machine translation model by the manually-aligned works of a particular translator. We make a detailed analysis of the effects of manual and automatic alignments, data augmentation methods, and corpus size on the translations. We propose an approach based on stylistic features to evaluate the style of a translator in the output translations. We show that the human translator style can be highly recreated in the target machine translations by adapting the models to the style of the translator.

Published
2023

6. Simultaneous interpreting experience enhances the use of case markers for prediction in Turkish.

Author

Özkan, Deniz, Hodzik, Ena, and Diriker, Ebru

Subjects
*TURKS, *SHORT-term memory, *FORECASTING, *INDIVIDUAL differences, *EYE tracking, *DECEPTION
Abstract

This study investigated individual differences in prediction during language comprehension in professional and student Turkish (A)–English (B) simultaneous interpreters as a function of simultaneous interpreting (SI) experience and working memory capacity (WMC). A Turkish visual-world eye-tracking prediction task examined whether the accusative versus the nominative case markers on the initial nouns of sentences could be used as cues to predicting an upcoming argument. The participants' WMC was measured using an automated operation span task. We found a prediction effect (a significantly higher increase in fixations to the plausible agent with time in the accusative than in the nominative condition) for the professionals, but not for the students. WMC contributed positively to the prediction effect in the professionals. Our findings indicate an SI-related advantage for prediction in professional versus student interpreters. Importantly, the present findings revealed an SI-related advantage in a prediction task that did not require interpreting. [ABSTRACT FROM AUTHOR]

Published
2023
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