Measuring Cognitive Effort with Translation Process Database

Wang Jiayi; Xiao Chenyixuan

doi:doi:10.11648/j.ijalt.20220804.13

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Measuring Cognitive Effort with Translation Process Database

Wang Jiayi, Xiao Chenyixuan

Published in International Journal of Applied Linguistics and Translation (Volume 8, Issue 4)

Received: 14 October 2022 Accepted: 7 November 2022 Published: 16 November 2022

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Abstract

The analysis and measurement of cognitive effort could be complicated when involved in translation production. And it therefore attracts researchers’ great attention to the investigation of this topic. Different from traditional data collection methods, the Translation Process Research Database (TPR-DB) utilizes the large corpus to record the translation process, including translation process data (e.g. keystrokes, fixations, mouse movements) and translation product data (e.g. ST, TT and links between tokens in both texts) from more than ten language pairs and dozens of translation and associated studies. After reviewing the studies and some findings on measuring cognitive effort with the TPR-DB, the present study proposes that features of HTra, HCross, AUs and PWR in the TPR-DB tables are frequently used as indicators for the measurement of cognitive effort during translation and post-editing processes. The attempts to measure cognitive effort with the TPR-DB have not only yielded some interesting findings but also added fresh insights to facilitate understanding and examination of cognitive effort. The present study pointed out that the TPR-DB provides a new and effective method to measure cognitive effort. It will further support and promote the future research in this field.

Published in	International Journal of Applied Linguistics and Translation (Volume 8, Issue 4)
DOI	10.11648/j.ijalt.20220804.13
Page(s)	148-152
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, TPR-DB, Measurement

References

[1]	Bangalore, S., Behrens, B., Carl, M., Ghankot, M., Heilmann, A., Nitzke, J., & Sturm, A. (2016). Syntactic variance and priming effects in translation. In In M. Carl, S. Bangalore & M. Schaeffer (Eds.), New directions in empirical translation process research (pp. 211-238). Springer, Cham.
[2]	Carl, M., & Jakobsen, A. L. (2009). Towards statistical modelling of translators’ activity data. International Journal of Speech Technology, 12 (4), 125-138.
[3]	Carl, M., Bangalore, S., & Schaeffer, M. (Eds.). (2016). New directions in empirical translation process research. London: Springer.
[4]	Carl, M. (Ed.). (2021). Explorations in empirical translation process research. Heidelberg: Springer.
[5]	Dragsted, B., & Carl, M. (2013). Towards a classification of translation styles based on eye-tracking and key-logging data. Journal of Writing Research, 5 (1), 133–158.
[6]	Hvelplund, K. T. (2016). Cognitive efficiency in translation. Reembedding translation process research, 128, 149-170.
[7]	Jensen, K. T. H., Sjørup, A. C., & Balling, L. W. (2009). Effects of L1 syntax on L2 translation. In I. M. Mees, F. Alves, & S. Göpferich (Eds.), Methodology, Technology and Innovation in Translation Process Research (pp. 319–336). Copenhagen: Samfundslitteratur.
[8]	Jensen, K. T. H. (2011). Distribution of attention between source text and target text during translation. In S. O’Brien (Ed.), Continuum Studies in Translation: Cognitive Explorations of Translation (pp. 215–236). London & New York, NY: Continuum.
[9]	Krings, H. P. (2001). Repairing Texts: Empirical Investigations of Machine Translation Post-Editing Processes. Kent, Ohio: Kent state university Press.
[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]	Lacruz, 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]	Lacruz, I., Ogawa, H., Yoshida, R., Yamada, M., & Ruiz Martinez, D. (2021). Using a product metric to identify differential cognitive effort in translation from Japanese to English and Spanish. In M, Carl (eds.), Explorations in Empirical Translation Process Research (pp. 295-314). Springer, Cham.
[14]	O’Brien, S. (2022). How to deal with errors in machine translation: Postediting. In D, Kenny (eds), Machine translation for everyone: Empowering users in the age of artificial intelligence, (pp. 105-120). Berlin: Language Science Press.
[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]	Schaeffer, M., & Carl, M. (2017). Language processing and translation. In Silvia Hansen-Schirra, Oliver Czulo & Sascha Hofmann (eds.), Empirical modelling of translation and interpreting, (pp. 117–154). Berlin: Language Science Press.

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APA Style

Wang Jiayi, Xiao Chenyixuan. (2022). Measuring Cognitive Effort with Translation Process Database. International Journal of Applied Linguistics and Translation, 8(4), 148-152. https://doi.org/10.11648/j.ijalt.20220804.13

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ACS Style

Wang Jiayi; Xiao Chenyixuan. Measuring Cognitive Effort with Translation Process Database. Int. J. Appl. Linguist. Transl. 2022, 8(4), 148-152. doi: 10.11648/j.ijalt.20220804.13

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AMA Style

Wang Jiayi, Xiao Chenyixuan. Measuring Cognitive Effort with Translation Process Database. Int J Appl Linguist Transl. 2022;8(4):148-152. doi: 10.11648/j.ijalt.20220804.13

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@article{10.11648/j.ijalt.20220804.13,
  author = {Wang Jiayi and Xiao Chenyixuan},
  title = {Measuring Cognitive Effort with Translation Process Database},
  journal = {International Journal of Applied Linguistics and Translation},
  volume = {8},
  number = {4},
  pages = {148-152},
  doi = {10.11648/j.ijalt.20220804.13},
  url = {https://doi.org/10.11648/j.ijalt.20220804.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijalt.20220804.13},
  abstract = {The analysis and measurement of cognitive effort could be complicated when involved in translation production. And it therefore attracts researchers’ great attention to the investigation of this topic. Different from traditional data collection methods, the Translation Process Research Database (TPR-DB) utilizes the large corpus to record the translation process, including translation process data (e.g. keystrokes, fixations, mouse movements) and translation product data (e.g. ST, TT and links between tokens in both texts) from more than ten language pairs and dozens of translation and associated studies. After reviewing the studies and some findings on measuring cognitive effort with the TPR-DB, the present study proposes that features of HTra, HCross, AUs and PWR in the TPR-DB tables are frequently used as indicators for the measurement of cognitive effort during translation and post-editing processes. The attempts to measure cognitive effort with the TPR-DB have not only yielded some interesting findings but also added fresh insights to facilitate understanding and examination of cognitive effort. The present study pointed out that the TPR-DB provides a new and effective method to measure cognitive effort. It will further support and promote the future research in this field.},
 year = {2022}
}

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TY  - JOUR
T1  - Measuring Cognitive Effort with Translation Process Database
AU  - Wang Jiayi
AU  - Xiao Chenyixuan
Y1  - 2022/11/16
PY  - 2022
N1  - https://doi.org/10.11648/j.ijalt.20220804.13
DO  - 10.11648/j.ijalt.20220804.13
T2  - International Journal of Applied Linguistics and Translation
JF  - International Journal of Applied Linguistics and Translation
JO  - International Journal of Applied Linguistics and Translation
SP  - 148
EP  - 152
PB  - Science Publishing Group
SN  - 2472-1271
UR  - https://doi.org/10.11648/j.ijalt.20220804.13
AB  - The analysis and measurement of cognitive effort could be complicated when involved in translation production. And it therefore attracts researchers’ great attention to the investigation of this topic. Different from traditional data collection methods, the Translation Process Research Database (TPR-DB) utilizes the large corpus to record the translation process, including translation process data (e.g. keystrokes, fixations, mouse movements) and translation product data (e.g. ST, TT and links between tokens in both texts) from more than ten language pairs and dozens of translation and associated studies. After reviewing the studies and some findings on measuring cognitive effort with the TPR-DB, the present study proposes that features of HTra, HCross, AUs and PWR in the TPR-DB tables are frequently used as indicators for the measurement of cognitive effort during translation and post-editing processes. The attempts to measure cognitive effort with the TPR-DB have not only yielded some interesting findings but also added fresh insights to facilitate understanding and examination of cognitive effort. The present study pointed out that the TPR-DB provides a new and effective method to measure cognitive effort. It will further support and promote the future research in this field.
VL  - 8
IS  - 4
ER  -

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Author Information

Wang Jiayi

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

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

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APA Style

Wang Jiayi, Xiao Chenyixuan. (2022). Measuring Cognitive Effort with Translation Process Database. International Journal of Applied Linguistics and Translation, 8(4), 148-152. https://doi.org/10.11648/j.ijalt.20220804.13

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ACS Style

Wang Jiayi; Xiao Chenyixuan. Measuring Cognitive Effort with Translation Process Database. Int. J. Appl. Linguist. Transl. 2022, 8(4), 148-152. doi: 10.11648/j.ijalt.20220804.13

Copy | Download

AMA Style

Wang Jiayi, Xiao Chenyixuan. Measuring Cognitive Effort with Translation Process Database. Int J Appl Linguist Transl. 2022;8(4):148-152. doi: 10.11648/j.ijalt.20220804.13

Copy | Download

@article{10.11648/j.ijalt.20220804.13,
  author = {Wang Jiayi and Xiao Chenyixuan},
  title = {Measuring Cognitive Effort with Translation Process Database},
  journal = {International Journal of Applied Linguistics and Translation},
  volume = {8},
  number = {4},
  pages = {148-152},
  doi = {10.11648/j.ijalt.20220804.13},
  url = {https://doi.org/10.11648/j.ijalt.20220804.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijalt.20220804.13},
  abstract = {The analysis and measurement of cognitive effort could be complicated when involved in translation production. And it therefore attracts researchers’ great attention to the investigation of this topic. Different from traditional data collection methods, the Translation Process Research Database (TPR-DB) utilizes the large corpus to record the translation process, including translation process data (e.g. keystrokes, fixations, mouse movements) and translation product data (e.g. ST, TT and links between tokens in both texts) from more than ten language pairs and dozens of translation and associated studies. After reviewing the studies and some findings on measuring cognitive effort with the TPR-DB, the present study proposes that features of HTra, HCross, AUs and PWR in the TPR-DB tables are frequently used as indicators for the measurement of cognitive effort during translation and post-editing processes. The attempts to measure cognitive effort with the TPR-DB have not only yielded some interesting findings but also added fresh insights to facilitate understanding and examination of cognitive effort. The present study pointed out that the TPR-DB provides a new and effective method to measure cognitive effort. It will further support and promote the future research in this field.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Measuring Cognitive Effort with Translation Process Database
AU  - Wang Jiayi
AU  - Xiao Chenyixuan
Y1  - 2022/11/16
PY  - 2022
N1  - https://doi.org/10.11648/j.ijalt.20220804.13
DO  - 10.11648/j.ijalt.20220804.13
T2  - International Journal of Applied Linguistics and Translation
JF  - International Journal of Applied Linguistics and Translation
JO  - International Journal of Applied Linguistics and Translation
SP  - 148
EP  - 152
PB  - Science Publishing Group
SN  - 2472-1271
UR  - https://doi.org/10.11648/j.ijalt.20220804.13
AB  - The analysis and measurement of cognitive effort could be complicated when involved in translation production. And it therefore attracts researchers’ great attention to the investigation of this topic. Different from traditional data collection methods, the Translation Process Research Database (TPR-DB) utilizes the large corpus to record the translation process, including translation process data (e.g. keystrokes, fixations, mouse movements) and translation product data (e.g. ST, TT and links between tokens in both texts) from more than ten language pairs and dozens of translation and associated studies. After reviewing the studies and some findings on measuring cognitive effort with the TPR-DB, the present study proposes that features of HTra, HCross, AUs and PWR in the TPR-DB tables are frequently used as indicators for the measurement of cognitive effort during translation and post-editing processes. The attempts to measure cognitive effort with the TPR-DB have not only yielded some interesting findings but also added fresh insights to facilitate understanding and examination of cognitive effort. The present study pointed out that the TPR-DB provides a new and effective method to measure cognitive effort. It will further support and promote the future research in this field.
VL  - 8
IS  - 4
ER  -

Copy | Download