Abstract
Translation process research (TPR) has advanced in the recent years to a state which allows us to study “in great detail what source and target text units are being processed, at a given point in time, to investigate what steps are involved in this process, what segments are read and aligned and how this whole process is monitored” (Alves 2015, p. 32). We have sophisticated statistical methods and with the powerful tools to produce a better and more detailed understanding of the underlying cognitive processes that are involved in translation. Following Jakobsen (2011), who suspects that we may soon be in a situation which allows us to develop a computational model of human translation, Alves (2015) calls for a “clearer affiliation between TPR studies and a particular cognitive sciences paradigm” (p. 23).
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Notes
- 1.
In the SMT literature, these terms are often used slightly differently. Decoding is referred to as “the task of finding the best translation for a given input sentence, according to a given translation model .” (http://www.statmt.org/survey/Topic/Decoding), i.e. the combination of what we have split into decoding (of ST) and encoding (of the TT). This usage of the term is most likely due to Weaver’s original proposal in 1949 that “we can take the Russian original as an encrypted version of the English plaintext”, suggesting that the Russian author had actually an English text in mind but encoded it in Russian. Decoding the Russian source text would thus lead to the actual message in English. While the term “encoding” is not common at all and would, most likely, correspond to a training phase in which the underlying MT models are trained, here we adopt the terminology uses in the relevance theoretic literature.
- 2.
The degree of variation can be measured by means of perplexity: a flat distribution of different translation realizations leads to high entropy perplexity while a very pointed distribution (i.e. many translators produce the same translation) leads to low perplexity values.
References
Alves, F. (2015). Translation process research at the interface: Paradigmatic, theoretical, and metodological issues in dialogue with cognitive science, expertise studies, and psycholinguistics. In A. Ferreira & J. W. Schwieter (Eds.), Psycholinguistic and cognitive inquiries into translation and interpreting (pp. 17–40). Amsterdam: John Benjamins.
Alves, F., & Gonçalves, J. L. (2003). A relevance theory approach to the investigation of inferential processes in translation. In F. Alves (Ed.), Triangulating translation: Perspectives in process oriented research (pp. 3–24). Amsterdam: John Benjamins.
Alves, F., Koglin, A., Mesa-Lao, B., Sekino, K., & Szpak, K. S. (2014, September 26–27). Investigating cognitive effort in post-editing: A relevance-theoretical approach. Paper presented at the International Conference on Eyetracking and Applied Linguistics, Warsaw.
Anderson, J. R. (2007). How can the human mind occur in the physical universe. New York: Oxford University Press.
Balling, L. W., Hvelplund, K. T., & Sjørup, A. C. (2014). Evidence of parallel processing during translation. Meta, 59(2), 234–259.
Bangalore, S., Behrens, B., Carl, M., Ghankot, M., Heilmann, A., Nitzke, J., et al. (2016). Syntactic variance and priming effects in translation. In New directions in empirical translation process research (pp. 211–238). Berlin: Springer.
Blakemore, D. (2002). Relevance and linguistic meaning: The semantics and pragmatics of discourse markers. Cambridge: Cambridge University Press.
Brown, P., Cocke, J., Pietra, S. D., Jelinek, F., Mercer, R., & Roossin, P. (1988, August 22–27). A statistical approach to language translation. Paper presented at the 12th conference on computational linguistics, COLING-88, Budapest, Hungary.
Brown, P., Pietra, V. D., Pietra, S. D., & Mercer, R. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2), 263–311.
Campbell, S. (2000). Choice network analysis in translation research. In M. Olohan (Ed.), Intercultural faultlines: Research models in translation studies: Textual and cognitive aspects (pp. 29–42). Manchester: St. Jerome.
Carl, M. (2010, November 18–19). A computational framework for a cognitive model of human translation processes. Paper presented at the translating and the computer 32, London. Retrieved from http://www.mt-archive.info/10/Aslib-2010-Carl.pdf
Carl, M., & Dragsted, B. (2012). Inside the monitor model: Processes of default and challenged translation production. Translation: Computation, Corpora, Cognition. Special Issue on the Crossroads Between Contrastive Linguistics, Translation Studies and Machine Translation, 2(1), 127–145.
Carl, M., Schaeffer, M., & Bangalore, S. (2016). The CRITT translation process research database. In M. Carl, S. Bangalore, & M. Schaeffer (Eds.), New directions in empirical translation process research (pp. 13–54). New York: Springer.
Čulo, O., Gutermuth, S., Hansen-Schirra, S., & Nitzke, J. (2014). The influence of post-editing on translation strategies. In S. O’Brien, L. W. Balling, M. Carl, M. Simard, & L. Specia (Eds.), Post-editing of machine translation: Processes and applications (pp. 200–219). Newcastle: Cambridge Scholars Publishing.
Gile, D. (1995). Basic concepts and models for interpreter and translator training. Amsterdam: John Benjamins.
Göpferich, S. (2009). Comprehensibility assessment using the Karlsruhe comprehensibility concept. The Journal of Specialised Translation, 11, 31–52.
Gutt, E. A. (1989). Translation and relevance. PhD thesis. University of London, London.
Gutt, E. A. (1990). A theoretical account of translation-without a translation theory. Target, 2(2), 135–164.
Gutt, E. A. (1991). Translation and relevance. Cognition and context. Oxford: Basil Blackwell.
Gutt, E. A. (2000). Translation and relevance. In Cognition and context (2nd ed.). Manchester: St. Jerome.
Hartsuiker, R. J., Pickering, M. J., & Veltkamp, E. (2004). Is syntax separate or shared between languages? Cross-linguistic syntactic priming in Spanish-English bilinguals. Psychological Science, 15(6), 409–414.
Hönig, H. G. (1991). Translation studies: The state of the art, the first James Holmes Symposium on Translation Studies. In Proceedings of the First James S Holmes symposium on translation studies (pp. 77–89). Amsterdam.
Hvelplund, K. T. (2011). Allocation of cognitive resources in translation: An eye-tracking and key-logging study. PhD thesis. Copenhagen Business School, Frederiksberg, Denmark.
Jakobsen, A. L. (2011). Tracking translators’ keystrokes and eye movements with Translog. In C. Alvstad, A. Hild, & E. Tiselius (Eds.), Methods and strategies of process research (pp. 37–55). Amsterdam: John Benjamins.
Jensen, K. T., Sjørup, A. C., & Balling, L. W. (2009). Effects of L1 syntax on L2 translation. In F. Alves, S. Göpferich, & I. M. Mees (Eds.), Methodology, technology and innovation in translation process research. A tribute to Arnt Lykke Jakobsen (pp. 319–336). Copenhagen: Samfundslitteratur.
Kliffer, M., & Stroinska, M. (2013). Relevance theory and translation. Linguistica Atlantica, 25, 165–172.
Läubli, S. (2014). Statistical modelling of human translation processes. Master’s thesis. University of Edinburgh, Edinburgh, UK.
Levy, R. (2008). A noisy-channel model of rational human sentence comprehension under uncertain input. In Proceedings of the conference on empirical methods in natural language processing (pp. 234–243). Honolulu: Association for Computational Linguistics.
Lörscher, W. (1992). Investigating the translation process. Meta, 37(3), 426–439.
Martínez-Gómez, P., Minocha, A., Huang, J., Carl, M., Bangalore, S., & Aizawa, A. (2014). Recognition of translator expertise using sequences of fixations and keystrokes. Paper presented at the Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA), Safety Harbor, FL.
Mylonakis, M., Sima’an, K., & Hwa, R. (2007). Unsupervised estimation for noisy-channel models. In Proceedings of the 24th international conference on machine learning (pp. 665–672). Corvalis: ACM.
Nagao, M. (1984). A framework of a mechanical translation between Japanese and English by analogy principle. In A. Elithorn & R. Beanerji (Eds.), Artificial and human intelligence: Edited review papers presented at the International NATO Symposium on Artificial and Human Intelligence, sponsored by the Special Programme Panel, held in Lyon, France, October 1981 (pp. 351–354). Amsterdam: North-Holland.
Sang, Z. (2006). A relevance theory perspective on translating the implicit information in literary texts. Journal of Translation, 2(2), 43–60.
Schaeffer, M., & Carl, M. (2013). Shared representations and the translation process: A recursive model. Translation and Interpreting Studies, 8(2), 169–190.
Schaeffer, M., Dragsted, B., Hvelplund, K. T., Balling, L. W., & Carl, M. (2016). Word translation entropy: Evidence of early target language activation during reading for translation. In M. Carl, S. Bangalore, & M. Schaeffer (Eds.), New directions in empirical translation process research (pp. 183–210). New York: Springer. isbn:978-3-319-20357-7.
Sekino, K. (2012). Investigating conceptual and procedural encodings in manual translation and in post-editing processes from Japanese to Portuguese.
Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communications. Urbana: University of Illinois Press.
Singla, K., Orrego-Carmona, D., Gonzales, A. R., Carl, M., & Bangalore, S. (2014). Predicting post-editor profiles from the translation process. Paper presented at the The 11th Conference of the Association for Machine Translation in the Americas.
Smith, K. (2002). Translation as secondary communication. The relevance theory perspective of Ernst-August Gutt. Acta Theologica, 22(1), 107–117.
Snover, M., Dorr, B., Schwartz, R., Micciulla, L., & Makhoul, J. (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 (pp. 223–231). Cambridge, MA.
Somers, H. (2003). An overview of EBMT. In M. Carl & A. Way (Eds.), Recent advances in example-based machine translation. Dordrecht: Kluwer Academic Publishers.
Sperber, D., & Wilson, D. (1993). Linguistic form and relevance. Lingua, 90(1), 1–25.
Sperber, D., & Wilson, D. (1995). Postface to the second edition of Relevance: Communication and cognition. Oxford: Blackwell.
Wilson, D. (1994). Relevance and understanding. In G. Brown, K. Malmkjær, A. Pollitt, & J. Williams (Eds.), Language and understanding (pp. 35–58). Oxford: Oxford University Press.
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Carl, M., Schaeffer, M. (2019). Outline for a Relevance Theoretical Model of Machine Translation Post-editing. In: Li, D., Lei, V., He, Y. (eds) Researching Cognitive Processes of Translation. New Frontiers in Translation Studies. Springer, Singapore. https://doi.org/10.1007/978-981-13-1984-6_3
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