Variability of multiple translations as evidence for cognitive and linguistic factors underlying translator decisions
Notice bibliographique
Résumé
While research in Translation Studies naturally relies on constructs and methods from various subfields of linguistics, there have been repeated calls for developing a unified theoretical framework and especially for improved interaction with cognitive linguistics and psycholinguistics (De Sutter & Lefer, 2020; Halverson & Kotze, 2021). In this context, Halverson’s (2017) revised “gravitational pull” (RGP) model aims to explain translators’ behavior in terms of usage-based factors such as the salience of source and target items and the entrenchment of translation pairs. However, the term “salience” remains notoriously ill-defined in linguistics, tending to conflate distinct phenomena such as attentional prominence, surprisal, and frequency (Boswijk & Coler, 2020; Schmid & Günther, 2016; see also Gilquin, 2008). Furthermore, salience may result not only from lifelong exposure but also from recent priming effects (Hartsuiker et al., 2016; De Sutter et al., 2021). In this talk, I discuss how the components of Halverson’s RGP model may be operationalized. As a test case, I present a study of French translations of English noun sequences (e.g., disaster relief program coordinator). Psycholinguists have long investigated how compound processing may elucidate the structure of the mental lexicon (Libben, 2005; Baayen et al., 2010; Gagné, 2011), and cross-linguistic research can extend this to bilingual cognition. Furthermore, the bare juxtaposition of nouns provides efficient information packing in English but poses specific challenges for translation into French (cf. Lefer & De Clerck, 2021). At the formal level, French tends to use prepositional post-modification, but the accumulation of prepositions can impose a significant cognitive load for longer sequences (e.g., disaster relief program coordinator → coordinateur du programme d'aide en cas de catastrophe). At the semantic level, potentially ambiguous relationships between constituents, left implicit in English, often need to be explicitated in French. I use data from the Multilingual Student Translation corpus (MUST; Granger & Lefer, 2020), consisting of French student translations for English texts specialized in sustainable finance. The resulting access to multiple translators’ interpretations and realizations makes it possible to explore translation variability, i.e., the number of different solutions for a given source instance. This approach, already championed by Malmkjær (1998), remains underexplored to date (but see Castagnoli, 2020). More specifically, I present results from two analyses: (1) a qualitative description of how translation solutions vary at three structural levels (main constituents, linking prepositions, and surface grammar), which can reveal different aspects of how translators interpret source ambiguity; and (2) quantitative multifactorial models of translation variability as a function of sequence length, lexicalization, frequency of use, and structural ambiguity. Thus, while previous RGP studies have explored the under- or overuse of particular lexical or grammatical forms in translation, I propose that studying the variability of multiple translations provides a complementary approach to elucidate the cognitive and linguistic factors underlying translator decisions. References Baayen, R. H., Victor Kuperman & Raymond Bertram. 2010. Frequency effects in compound processing. In Sergio Scalise & Irene Vogel (eds.), Cross-Disciplinary Issues in Compounding, vol. 311, 257–270. Amsterdam: John Benjamins Publishing Company. https://doi.org/10.1075/cilt.311.20baa. Boswijk, Vincent & Matt Coler. 2020. What is Salience? Open Linguistics. De Gruyter Open Access 6(1). 713–722. https://doi.org/10.1515/opli-2020-0042. Castagnoli, Sara. 2020. Translation choices compared: Investigating variation in a learner translation corpus. In Sylviane Granger & Marie-Aude Lefer (eds.), Translating and comparing languages: Corpus-based insights. Selected proceedings of the Fifth Using Corpora in Contrastive and Translation Studies Conference (Corpora and Language in Use - Proceedings), 25–44. Louvain-La-Neuve: Presses Universitaires. 569 De Sutter, Gert, Timothy Colleman & Anne-Sophie Ghyselen. 2021. Intra- and inter-textual syntactic priming in original and translated English. In Gitte Kristiansen, Karlien Franco, Stefano De Pascale, Laura Rosseel & Weiwei Zhang (eds.), Cognitive Sociolinguistics Revisited, 410–421. De Gruyter. https://doi.org/10.1515/9783110733945. De Sutter, Gert & Marie-Aude Lefer. 2020. On the need for a new research agenda for corpus-based translation studies: A multi-methodological, multifactorial and interdisciplinary approach. Perspectives 28(1). 1–23. https://doi.org/10.1080/0907676X.2019.1611891. Gagné, Christina L. 2011. Psycholinguistic perspectives. In Rochelle Lieber & Pavol Stekauer (eds.), The Oxford Handbook of Compounding, 255–271. Oxford University Press. Gilquin, Gaëtanelle. 2008. What you think ain’t what you get: Highly polysemous verbs in mind and language. In Jean-Rémi Lapaire, Guillaume Desagulier & Jean-Baptiste Guignard (eds.), Du fait grammatical au fait cognitif / From gram to mind: grammar as cognition, 235–255. Pessac: Presses universitaires de Bordeaux. Granger, Sylviane & Marie-Aude Lefer. 2020. The Multilingual Student Translation corpus: a resource for translation teaching and research. Language Resources and Evaluation 54(4). 1183–1199. https://doi.org/10.1007/s10579-020-09485-6. Halverson, Sandra L. 2017. Gravitational pull in translation: Testing a revised model. In Gert De Sutter, Marie-Aude Lefer & Isabelle Delaere (eds.), Empirical translation studies: New methodological and theoretical traditions (Trends in Linguistics. Studies and Monographs, Vol. 300), 9–45. De Gruyter Mouton. http://hdl.handle.net/1854/LU-7222960. Halverson, Sandra L. & Haidee Kotze. 2021. Sociocognitive constructs in Translation and Interpreting Studies (TIS): Do we really need concepts like norms and risk when we have a comprehensive usage-based theory of language? In Sandra L. Halverson & Álvaro Marín García (eds.), Contesting Epistemologies in Cognitive Translation and Interpreting Studies, 51–79. Routledge. Hartsuiker, Robert J., Saskia Beerts, Maaike Loncke, Timothy Desmet & Sarah Bernolet. 2016. Crosslinguistic structural priming in multilinguals: Further evidence for shared syntax. Journal of Memory and Language 90. 14–30. https://doi.org/10.1016/j.jml.2016.03.003. Lefer, Marie-Aude & Marie De Clerck. 2021. L’apport des corpus intermodaux en lexicologie contrastive : Étude comparative de la traduction écrite et de l’interprétation simultanée des séquences de noms. In Sylvie Hanote & Raluca Nita (eds.), Morphophonologie, lexicologie et langue de spécialité: hommage à Jean-Louis Duchet et à Michel Paillard, 145–162. Rennes: Presses universitaires de Rennes. Libben, Gary. 2005. Everything is Psycholinguistics: Material and Methodological Considerations in the Study of Compound Processing. Canadian Journal of Linguistics/Revue canadienne de linguistique. Cambridge University Press 50(1–4). 267–283. https://doi.org/10.1017/S000841310000373X. Malmkjaer, Kirsten. 1998. Love thy neighbour: Will parallel corpora endear linguists to translators? Meta : journal des traducteurs / Meta: Translators’ Journal 43(4). 534–541. https://doi.org/10.7202/003545ar. Schmid, Hans-Jörg & Franziska Günther. 2016. Toward a unified socio-cognitive framework for salience in language. Frontiers in Psychology 7. 1110. https://www.frontiersin.org/articles/10.3389/fpsyg.2016.01110.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,141 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».