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In:Translation in Transition: Human and machine intelligence
Edited by Isabel Lacruz
[American Translators Association Scholarly Monograph Series XX] 2023
► pp. 1–14

Chapter 1
Translation in transition

Human and machine intelligence

 | Kent State University
Published online: 26 July 2023
DOI logo with link
https://doi.org/10.1075/ata.xx.01lac
Abstract
We provide an overview of the contributions to this volume, elaborating on how they relate to each other and to the overall theme.
References (25)
References
ALPAC, Automatic Language Processing Advisory Committee. 1966. Language and Machines: Computers in translation and linguistics. Publication 1416. Washington, DC: National Research Council.Google Scholar logo with link to Google Scholar
Banerjee, Satanjeev, and Alon Lavie. 2005. “METEOR: An automatic metric for MT evaluation with improved correlation with human judgments.” In Proceedings of the ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, 65–72.Google Scholar logo with link to Google Scholar
Carl, Michael, and Barbara Dragsted. 2012. “Inside the monitor model: Processes of default and challenged translation production”. Translation: Corpora, Computation, Cognition 2 (1): 127–145.Google Scholar logo with link to Google Scholar
De Groot, Annette M. B. 1997. “The cognitive study of translation and interpretation: Three approaches.” In Cognitive processes in translation and interpreting, ed. by Joseph Danks, Gregory Shreve, Stephen Fountain, and Michael McBeath, 25–56. Thousand Oaks, CA: Sage Publications, Inc.Google Scholar logo with link to Google Scholar
Englund Dimitrova, Birgitta. 2005. Expertise and Explicitation in the Translation Process. Amsterdam/Philadelphia: John Benjamins. Google Scholar logo with link to Google Scholar
Hutto, Daniel D., and Erik Myin. 2017. Evolving enactivism: Basic minds meet content. Cambridge, MA: MIT press. Google Scholar logo with link to Google Scholar
Ilg, Gérard, and Sylvie Lambert. 1996. “Teaching consecutive interpreting.” Interpreting 1(1): 69–99. Google Scholar logo with link to Google Scholar
Kiraly, Don. 2013. “Towards a view of translator competence as an emergent phenomenon: Thinking outside the box (es) in translator education.” New prospects and perspectives for educating language mediators, ed. by Donald Kiraly, Silvia Hansen-Schirra, and Karin Maksymski, 197–224. Tūbingen: Narr.Google Scholar logo with link to Google Scholar
Krings, Hans P. 2001. Repairing texts: Empirical investigations of machine translation post-editing processes. Vol. 5. Kent, OH: Kent State University Press.Google Scholar logo with link to Google Scholar
Lacruz, Isabel. 2017. “Cognitive effort in translation, editing and post-editing.” In Handbook of Translation and Cognition, ed. by John W. Schwieter, and Aline Ferreira, 386–401. Oxford: Blackwell. Google Scholar logo with link to Google Scholar
Lacruz, Isabel, and Riitta Jääskeläinen (eds.). 2018. Innovation and expansion in translation process research. Volume XVIII of the American Translators Association Scholarly Monograph series. Amsterdam/Philadelphia: John Benjamins. Google Scholar logo with link to Google Scholar
Lacruz, Isabel, Michael Denkowski, and Alon Lavie. 2014. “Cognitive demand and cognitive effort in post-editing.” In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas, 73–84.Google Scholar logo with link to Google Scholar
Macizo, Pedro, and M. Teresa Bajo. 2006. “Reading for repetition and reading for translation: Do they involve the same processes?.” Cognition 99(1): 1–34. Google Scholar logo with link to Google Scholar
Muñoz, Ricardo. 2016. “Of minds and men – computers and translators.” Poznan Studies in Contemporary Linguistics 52 (2), 351–381.Google Scholar logo with link to Google Scholar
Omar, Abdulfattah, and Yasser Gomaa. 2020. “The machine translation of literature: Implications for translation pedagogy.” International Journal of Emerging Technologies in Learning (IJET) 15(11): 228–235. Google Scholar logo with link to Google Scholar
Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. “Bleu: a method for automatic evaluation of machine translation.” In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, 311–318.Google Scholar logo with link to Google Scholar
Schaeffer, Moritz, Barbara Dragsted, Kristian Tangsgaard Hvelplund, Laura Winther Balling, and Michael Carl. 2016. “Word translation entropy: Evidence of early target language activation during reading for translation.” In New directions in empirical translation process research, 183–210. Berlin: Springer, Cham. Google Scholar logo with link to Google Scholar
Schaeffer, Moritz, and Carl, Michael. 2013, 2015. “Shared Representations and the Translation Process: A Recursive Model.” In Translation and Interpreting Studies, 8(2),169–190. Reprint in Describing Cognitive Processes in Translation: Acts and Events, ed. by Maureen Ehrensberger-Dow, Birgitta Englund Dimitrova, Séverine Hubscher-Davidson, and Ulf Norberg. Benjamins Current Topics, 77. Amsterdam: John Benjamins. Google Scholar logo with link to Google Scholar
Schwartz, Lane. 2018. “The history and promise of machine translation.” In Innovation and expansion in translation process research, ed. by Isabel Lacruz, and Riitta Jāāskelāinen, 161–190. Amsterdam: John Benjamins. Google Scholar logo with link to Google Scholar
Seleskovitch, Danica. 1976. “Interpretation, a psychological approach to translating.” In Translation: Applications and Research, ed. by R. Bruce, W. Anderson, and Richard W. Brislin, 92–116. New York, NY: Gardner Press.Google Scholar logo with link to Google Scholar
Snover, Matthew, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006. “A study of translation edit rate with targeted human annotation.” In Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, 223–231.Google Scholar logo with link to Google Scholar
Tirkkonen-Condit, Sonja. 2005. “The monitor model revisited: Evidence from process research.” Meta 50(2): 405–414. Google Scholar logo with link to Google Scholar
Toral, Antonio, and Andy Way. 2018. “What level of quality can neural machine translation attain on literary text?” In Translation Quality Assessment, 263–287. Berlin: Springer, Cham. Google Scholar logo with link to Google Scholar
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