Measuring Student Translators’ Cognitive Effort with Pauses: A Comparative Analysis of Human Translation and MT Post-Editing

Wang Jiayi

doi:doi:10.11648/j.cls.20220804.14

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Measuring Student Translators’ Cognitive Effort with Pauses: A Comparative Analysis of Human Translation and MT Post-Editing

Wang Jiayi

Published in Communication and Linguistics Studies (Volume 8, Issue 4)

Received: 10 November 2022 Accepted: 1 December 2022 Published: 8 December 2022

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Abstract

Measuring the cognitive effort involved in the translation production is one of the most important issues in translation and in MT post-editing. The present study investigated student translators’ cognitive effort with pauses by comparing their processes in human translation and MT post-editing. Translog II, a keyboard recording software, was used to record the translation process data. Sixteen sophomores majoring in English participated in the experiment. Mean duration of processing time and average pause duration per word under different thresholds (TG300, TG500, TG1000, TG2000 and TG5000) were used as indicators to measure cognitive effort. The results show that students translators tend to perform post-editing tasks faster than human translation, and their post-editing processes need less cognitive effort than human translation as indicated by less mean duration processing time and shorter average pause duration per word under the thresholds of 300 ms, 500 ms and 1000 ms (TG300, TG500, TG1000). It is worth mentioning that when the thresholds of pauses are longer, reaching 2000 ms or more, there is no significant difference between the two tasks for student translators. In the process of post-editing, the student translators were more concerned about machine translation text, mainly for checking and correcting machine translation errors, especially grammatical errors; while in the process of human translation, they invested more cognitive effort to understand the source text.

Published in	Communication and Linguistics Studies (Volume 8, Issue 4)
DOI	10.11648/j.cls.20220804.14
Page(s)	80-84
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Cognitive Effort, Pause, Post-Editing, Human Translation

References

[1]	Carl, M., Dragsted, B., Elming, J., Hardt, D., & Jakobsen, A. L. (2011). The process of post-editing: a pilot study. Copenhagen Studies in Language, 41, 131-142.
[2]	Carl, M., Bangalore, S., and Schaeffer, M. (2016). New Directions in Empirical Translation Process Research. Exploring the TPR-DB. New York: Springer.
[3]	Daems, J., Vandepitte, S., Hartsuiker, R., & Macken, L. (2015). The impact of machine translation error types on post-editing effort indicators. In S. O’Brien & M. Simard (Eds.), Proceedings of the 4th workshop on post-editing technology and practice (pp. 31–45). Miami: Association for Machine Translation in the Americas.
[4]	Dragsted, B., & Hansen, I. (2009). Exploring translation and interpreting hybrids. The case of sight translation. Meta: Journal des traducteurs/Meta: Translators' Journal, 54 (3), 588-604.
[5]	Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87 (4), 329–354.
[6]	Guerberof, A. (2009). Productivity and quality in the post-editing of outputs from translation memories and machine translation. Localization Focus. 7 (1), 11-21.
[7]	Kellogg, R. T. (1987). Effects of topic knowledge on the allocation of processing time and cognitive effort to writing processes. Memory & Cognition, 15 (3), 256–266.
[8]	Krings, H. P. (2001). Repairing Texts: Empirical Investigations of Machine Translation Post-Editing Processes. Kent, Ohio: Kent state university Press.
[9]	Lacruz, I., Shreve, G. M., & Angelone, E. (2012). Average pause ratio as an indicator of cognitive effort in post-editing: A case study. In S. O’Brien, M. Simard, & L. Specia (Eds.), Proceedings of the AMTA 2012 Workshop on Post-Editing Technology and Practice (WTTP) (pp. 29-38). San Diego: Association for Machine Translation in the Americas.
[10]	Lacruz, I., & Shreve, G. M. (2014). Pauses and cognitive effort in post-editing. In S. O’Brien, L. Winther Balling, M. Carl, M. Simard, & L. Specia (Eds.), Post-Editing of Machine Translation: Processes and Applications (pp. 244-272). Cambridge: Cambridge Scholars Publishing.
[11]	Lcruz, I. (2017). Cognitive effort in translation, editing, and post-editing. In J. W. Schwieter & A. Ferreira (Eds.), The handbook of translation and cognition (pp. 386-401). Hoboken: Wiley-Blackwell.
[12]	Lacruz I, Carl M, Yamada M. (2018). Literality and cognitive effort: Japanese and Spanish. In A, Calzolari (eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (pp. 3818-3821). Paris: European Language Resources Association.
[13]	Lu, Z., & Sun, J. (2018). An eye-tracking study of cognitive processing in human translation and post-editing. Foreign Language Teaching and Research, 50 (5), 760-769.
[14]	O’Brien, S. (2006). Pauses as indicators of cognitive effort in post-editing machine translation output. Across Languages and Cultures, 7 (1), 1–21.
[15]	Schaeffer, M., Carl, M., Lacruz, I., & Aizawa, A. (2016). Measuring Cognitive Translation Effort with Activity Units. Baltic Journal of Modern Computing, 4 (2), 331–345.
[16]	Schumacher, G. M., Klare, G. r., Cronin, F. C., & Moses, J. D. (1984). Cognitive activities of beginning and advanced college writers: A pausal analysis. Research in the Teaching of English. 169–187.
[17]	Tirkkonen-Condit, S. (1990). Professional vs. non-professional translation: A think-aloud protocol study. Learning, keeping and using language, 2, 381-394.
[18]	Tyler, S. W., Hertel, P. T., McCallum, M. C., & Ellis, H. C. (1979). Cognitive effort and memory. Journal of Experimental Psychology: Human Learning and Memory, 5, 607–617.

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Wang Jiayi. (2022). Measuring Student Translators’ Cognitive Effort with Pauses: A Comparative Analysis of Human Translation and MT Post-Editing. Communication and Linguistics Studies, 8(4), 80-84. https://doi.org/10.11648/j.cls.20220804.14

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ACS Style

Wang Jiayi. Measuring Student Translators’ Cognitive Effort with Pauses: A Comparative Analysis of Human Translation and MT Post-Editing. Commun. Linguist. Stud. 2022, 8(4), 80-84. doi: 10.11648/j.cls.20220804.14

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AMA Style

Wang Jiayi. Measuring Student Translators’ Cognitive Effort with Pauses: A Comparative Analysis of Human Translation and MT Post-Editing. Commun Linguist Stud. 2022;8(4):80-84. doi: 10.11648/j.cls.20220804.14

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@article{10.11648/j.cls.20220804.14,
  author = {Wang Jiayi},
  title = {Measuring Student Translators’ Cognitive Effort with Pauses: A Comparative Analysis of Human Translation and MT Post-Editing},
  journal = {Communication and Linguistics Studies},
  volume = {8},
  number = {4},
  pages = {80-84},
  doi = {10.11648/j.cls.20220804.14},
  url = {https://doi.org/10.11648/j.cls.20220804.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cls.20220804.14},
  abstract = {Measuring the cognitive effort involved in the translation production is one of the most important issues in translation and in MT post-editing. The present study investigated student translators’ cognitive effort with pauses by comparing their processes in human translation and MT post-editing. Translog II, a keyboard recording software, was used to record the translation process data. Sixteen sophomores majoring in English participated in the experiment. Mean duration of processing time and average pause duration per word under different thresholds (TG300, TG500, TG1000, TG2000 and TG5000) were used as indicators to measure cognitive effort. The results show that students translators tend to perform post-editing tasks faster than human translation, and their post-editing processes need less cognitive effort than human translation as indicated by less mean duration processing time and shorter average pause duration per word under the thresholds of 300 ms, 500 ms and 1000 ms (TG300, TG500, TG1000). It is worth mentioning that when the thresholds of pauses are longer, reaching 2000 ms or more, there is no significant difference between the two tasks for student translators. In the process of post-editing, the student translators were more concerned about machine translation text, mainly for checking and correcting machine translation errors, especially grammatical errors; while in the process of human translation, they invested more cognitive effort to understand the source text.},
 year = {2022}
}

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TY  - JOUR
T1  - Measuring Student Translators’ Cognitive Effort with Pauses: A Comparative Analysis of Human Translation and MT Post-Editing
AU  - Wang Jiayi
Y1  - 2022/12/08
PY  - 2022
N1  - https://doi.org/10.11648/j.cls.20220804.14
DO  - 10.11648/j.cls.20220804.14
T2  - Communication and Linguistics Studies
JF  - Communication and Linguistics Studies
JO  - Communication and Linguistics Studies
SP  - 80
EP  - 84
PB  - Science Publishing Group
SN  - 2380-2529
UR  - https://doi.org/10.11648/j.cls.20220804.14
AB  - Measuring the cognitive effort involved in the translation production is one of the most important issues in translation and in MT post-editing. The present study investigated student translators’ cognitive effort with pauses by comparing their processes in human translation and MT post-editing. Translog II, a keyboard recording software, was used to record the translation process data. Sixteen sophomores majoring in English participated in the experiment. Mean duration of processing time and average pause duration per word under different thresholds (TG300, TG500, TG1000, TG2000 and TG5000) were used as indicators to measure cognitive effort. The results show that students translators tend to perform post-editing tasks faster than human translation, and their post-editing processes need less cognitive effort than human translation as indicated by less mean duration processing time and shorter average pause duration per word under the thresholds of 300 ms, 500 ms and 1000 ms (TG300, TG500, TG1000). It is worth mentioning that when the thresholds of pauses are longer, reaching 2000 ms or more, there is no significant difference between the two tasks for student translators. In the process of post-editing, the student translators were more concerned about machine translation text, mainly for checking and correcting machine translation errors, especially grammatical errors; while in the process of human translation, they invested more cognitive effort to understand the source text.
VL  - 8
IS  - 4
ER  -

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Author Information

Wang Jiayi

College of Foreign Languages, Hunan Institute of Engineering, Xiangtan, China

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Plain Text BibTeX RIS

APA Style

Wang Jiayi. (2022). Measuring Student Translators’ Cognitive Effort with Pauses: A Comparative Analysis of Human Translation and MT Post-Editing. Communication and Linguistics Studies, 8(4), 80-84. https://doi.org/10.11648/j.cls.20220804.14

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ACS Style

Wang Jiayi. Measuring Student Translators’ Cognitive Effort with Pauses: A Comparative Analysis of Human Translation and MT Post-Editing. Commun. Linguist. Stud. 2022, 8(4), 80-84. doi: 10.11648/j.cls.20220804.14

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AMA Style

Wang Jiayi. Measuring Student Translators’ Cognitive Effort with Pauses: A Comparative Analysis of Human Translation and MT Post-Editing. Commun Linguist Stud. 2022;8(4):80-84. doi: 10.11648/j.cls.20220804.14

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@article{10.11648/j.cls.20220804.14,
  author = {Wang Jiayi},
  title = {Measuring Student Translators’ Cognitive Effort with Pauses: A Comparative Analysis of Human Translation and MT Post-Editing},
  journal = {Communication and Linguistics Studies},
  volume = {8},
  number = {4},
  pages = {80-84},
  doi = {10.11648/j.cls.20220804.14},
  url = {https://doi.org/10.11648/j.cls.20220804.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cls.20220804.14},
  abstract = {Measuring the cognitive effort involved in the translation production is one of the most important issues in translation and in MT post-editing. The present study investigated student translators’ cognitive effort with pauses by comparing their processes in human translation and MT post-editing. Translog II, a keyboard recording software, was used to record the translation process data. Sixteen sophomores majoring in English participated in the experiment. Mean duration of processing time and average pause duration per word under different thresholds (TG300, TG500, TG1000, TG2000 and TG5000) were used as indicators to measure cognitive effort. The results show that students translators tend to perform post-editing tasks faster than human translation, and their post-editing processes need less cognitive effort than human translation as indicated by less mean duration processing time and shorter average pause duration per word under the thresholds of 300 ms, 500 ms and 1000 ms (TG300, TG500, TG1000). It is worth mentioning that when the thresholds of pauses are longer, reaching 2000 ms or more, there is no significant difference between the two tasks for student translators. In the process of post-editing, the student translators were more concerned about machine translation text, mainly for checking and correcting machine translation errors, especially grammatical errors; while in the process of human translation, they invested more cognitive effort to understand the source text.},
 year = {2022}
}

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TY  - JOUR
T1  - Measuring Student Translators’ Cognitive Effort with Pauses: A Comparative Analysis of Human Translation and MT Post-Editing
AU  - Wang Jiayi
Y1  - 2022/12/08
PY  - 2022
N1  - https://doi.org/10.11648/j.cls.20220804.14
DO  - 10.11648/j.cls.20220804.14
T2  - Communication and Linguistics Studies
JF  - Communication and Linguistics Studies
JO  - Communication and Linguistics Studies
SP  - 80
EP  - 84
PB  - Science Publishing Group
SN  - 2380-2529
UR  - https://doi.org/10.11648/j.cls.20220804.14
AB  - Measuring the cognitive effort involved in the translation production is one of the most important issues in translation and in MT post-editing. The present study investigated student translators’ cognitive effort with pauses by comparing their processes in human translation and MT post-editing. Translog II, a keyboard recording software, was used to record the translation process data. Sixteen sophomores majoring in English participated in the experiment. Mean duration of processing time and average pause duration per word under different thresholds (TG300, TG500, TG1000, TG2000 and TG5000) were used as indicators to measure cognitive effort. The results show that students translators tend to perform post-editing tasks faster than human translation, and their post-editing processes need less cognitive effort than human translation as indicated by less mean duration processing time and shorter average pause duration per word under the thresholds of 300 ms, 500 ms and 1000 ms (TG300, TG500, TG1000). It is worth mentioning that when the thresholds of pauses are longer, reaching 2000 ms or more, there is no significant difference between the two tasks for student translators. In the process of post-editing, the student translators were more concerned about machine translation text, mainly for checking and correcting machine translation errors, especially grammatical errors; while in the process of human translation, they invested more cognitive effort to understand the source text.
VL  - 8
IS  - 4
ER  -

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