This paper focuses on designing a series of experiments based on the discourse and communication translation model. We compare the model based and non-model based translations and metadata organized in line with the model categories. Empirical data is compared for different subgroups of participants
Keywords: Translation experimentdiscourse and communication translation modeltranslation strategydiscursive profiletranslation modelempirical translation research
The shift from a textocentric to the anthropocentric approach in contemporary translation theory advocates for empirical translation studies, and in order to incorporate empirical data into the existing interdisciplinary framework we should place the linguistic (translation) experiment within the existing translation research framework. Over the last few decades empirical translation research has clearly gained momentum, addressing methodologies used to describe the translation process at large, principles of designing and implementing empirical translation research (Sutter et al., 2017), (Hansen-Schirra et al., 2017), specific methods of empirical research (e. g., TAP, think-aloud protocols) and empirical corpus-based research in translation.
Research questions in empirical translation studies overlap with those in translation quality assessment and professional translator training, methodologies and metalanguages of translation studies and linguistics at large (cf. Chudinov, 2015), encompassing empirical research and linguistic experiments in cognitive linguistics, psycholinguistics, and education science, and require a systematic description of design patterns and procedures in empirical translation research, as well as a theoretical framework for and typology of translation experiments.
“As a method of analysis, discourse analysis is holistic, dealing with entire constituents of an act of communication. It is a method that studies a discourse in its context of culture, context of situation, its structure and individual constituents. It provides a model for uncovering patterns of choice and relating them to specific concerns and contexts in which the translator works” (Munday et al., 2017: 3). Our current research is built around the discourse and communication translation model (Volkova, 2012), (Volkova, 2014), which is a combination of the following interrelated levels:
Textual level: linguistic parameters of a source text (lexical and semantic, syntactic, stylistic and pragmatic features), ST discursive characteristics (authorship, addressness, and narrative).
Discursive level: discourse nodal points, goals, values, field, tenor, mode and chronotopos, interdiscursivity (polydiscursivity).
Communicative level: communicative functions, typical features and strategies of communication.
Reality level: domain-specific translation environment and ST subject field.
The model remains open to additional parameters that fall into respective categories in the framework of the selected approach.
Parameters pertaining to each level define translation solutions and build up translation microstrategies on the textual level and translation macrostrategies on discursive and communicative levels at various stages of the translation process. The discourse and communication translation model and the model parameters serve as the basis for the discourse and communication approach to translation: interdisciplinary, multi-layered and open to data from discourse studies, pragmatics, sociolinguistics, cross-cultural communication and other lines of research.
We have introduced the concept of a “discursive profile” that is the basis for working out a translation strategy. A profile is composed for a particular source text, but may also be characterized by some universal features. In the framework of a larger piece of research, we have revised the concept of a translation strategy, described the interrelation between a translation model and a translation strategy, and put together an algorithm for working out a translation strategy. Translation strategy is a flexible and, to a certain extent, individual (but not unique) algorithm implemented for a particular source text.
To test the model at this stage, we develop a series of translation experiments and, basing on the empirical data and metadata elucidated from translations and translators’ comments, compare the model based and non-model based translation solutions used by trainee translators, the translation errors that they make, as well as the algorithms that they use to work out a translation strategy and apply the discourse and communication translation model when translating.
The broader framework for designing and carrying out the experiment encompasses the following research questions:
Organize the basic principles and forms of empirical translation studies in contemporary Russian and international research.
Describe methodological principles of designing, carrying out and analyzing a series of translation experiments based on the discourse and communication translation model, identify experimental groups, develop the experimental package and guidelines for participants, and carry out a series of experiments.
Analyze experimental data and compare model based and non-model based translations and the translators’ comments on their translation strategies and the model per se. Analyze translation solutions, translation errors, translation strategies and practical applications of the discourse and communication translation model.
Organize the experimental results according to the model categories and parameters and compare the data for various groups of participants.
Integrate empirical data into our concept at large, adjusting the discourse and communication translation model accordingly if needed. For instance, the following concepts can be revised basing on empirical data: “translation brief”, “translation purpose”, “translation dominant”, “translator’s overall intention”, “translation model”, “translation strategy”; the implications and practical applications of microstrategies and macrostrategies within a translation strategy, etc.
Purpose of the Study
This study focuses specifically on one of the research questions above, namely designing a series of experiments based on the discourse and communication translation model and tackling potential methodological challenges.
Methodological framework for this research encompasses the linguistic approach, communicative and functional approach, cognitive and activity-based approach, and the suggested discourse and communicative approach to describing the process of translation; methods of empirical translation research; general and specific analysis methods and techniques: modelling and comparative analysis, logical, hypothetico-deductive and descriptive methods, as well as introspection; componential analysis, systemic-functional analysis, contextual analysis, and transformational analysis; discourse analysis and the suggested discourse and communication translation model.
As this study is focused specifically on the experiment design, we shall describe the data, design, and procedure, briefly outlining the current hypothesis and some methodological issues.
Participating in the series of experiments are trainee translators (bachelor’s and master’s degree students enrolled in respective English-Russian translation programs at Chelyabinsk State University) who translate a text without using the discourse and communication translation model first (TR1), and then translate the same text again applying the model (TR2).
The students’ mother tongue is Russian, and they are given a text to translate from English into Russian. The source text is a smartphone review, 263 words, to be supposedly translated for the reviews section of the company’s official website. The participants are given written guidelines on the model application before they proceed with TR2. However, neither examples of the model parameters nor samples of translation strategies are provided at this stage, so that the translators’ opinions are not immediately influenced by ours. Shortly after the start of the experiment some procedural issues are dealt with in a group question and answer session. Participants are supposed to translate the text completely and submit their translations via email or, for a particular subgroup of translators, post their target texts online in a closed user group forum solely available for the experiment participants for writing and commenting. Translators have two weeks to complete the task, and within these two weeks no time limit is set for translation.
Translators are required to comment on their translation solutions that have been reconsidered after applying the model (i. e., edit their own TR1 heeding the model parameters), describe in writing a discursive profile and their final translation strategy with the model applied, additionally comment on their peers’ translations comparing TR1 and TR2, respectively, and finally comment on the model application per se: we elucidate data on how the experiment participants use the model when translating, how their analysis relates to the translation brief, whether they use any additional resources to define or interpret the model parameters, etc.
The experimental package therefore includes the following:
Target texts, ver. 1.0; the trainees’ translation strategies, ver. 1.0, with individual comments on their translation solutions; a list of resources used while translating; the trainees’ peer comments, ver. 1.0, on the decisions made by other translators in their subgroup.
Target texts, ver. 2.0; discursive profiles; the trainees’ translation strategies, ver. 2.0, with individual comments on their (revised) translation solutions; trainees’ peer comments, ver. 2.0, on the decisions made by other translators in their subgroup; comments on the model application (metadata).
Our task is to compare the empirical data elucidated for all the groups using both versions of the target text (TR1, TR2) and various comments provided by each participant, i. e., data elucidated from sources other than the experimenter’s introspection for the quality assessment of model based and non-model based translations.
The current research hypothesis runs as follows: using the model to work out a translation strategy improves overall TT quality and / or improves the quality of translation with regard to certain model parameters. The quality of model based translations is higher than that of non-model based translations, as can be concluded from the analysis of complete target texts and the translation solutions applied to the ST “diagnostic points” (cf. Dobrovol’skij, 2003, also termed “sensitive points”), or markers, as well as the analysis of the translators’ individual comments (what trainee translators manage to achieve when applying the model) and peer comments (higher explicit evaluations of a peer’s TR2 as compared to TR1 in respective subgroups).
We need yet to find out what specific TT parameters are enhanced with the model applied, what parameters are influenced and what are not influenced by the model, and how the model parameters are connected to other more global ST / TT features. E. g., the model allows for a more relevant transfer of the recipient factor or the text type (genre) when translating, but cannot compensate for the lack of the mother tongue proficiency or the overall professional competence of the translator.
Designing a series of experiments based on the discourse and communication translation model in a way described above should help solve the main task of our current empirical research: elucidate empirical data to demonstrate the correlation between a translation strategy and the discourse and communication translation model and compare model based and non-model based translations. This experiment is in line with contemporary “product- and process-based translation research” (Hansen-Schirra et al., 2017: viii) and may contribute both to translation theory and translator training. By way of conclusion we would like to quote a passage from a paper on translation solution types (Pym et al., 2015). Pym and Torres-Simón describe, inter alia, how students “reflect on the relation between translating and theorizing” (Pym et al., 2015: 102): “some of the students proposed changes to make the typologies ‘practical and useful for translators [since] translators don’t want to spend too much time learning those categories yet find them difficult to apply’” (Ibid.). “When they try to categorize their translation solutions, students reflect both on their work and on the difficulties of theorization” (Ibid.: 101), and “teaching and trying to apply solution types in the translation class brings benefits not only for the training and self-training of students but also, hopefully, for the development of translation theory” (Ibid.: 102).
This study was supported by the Ministry of Education and Science of the Russian Federation (project No. 34.6111.2017/БЧ, “Translating Media Texts within the Context of Modern Tendencies in Mass Communication”).
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30 April 2018
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Sociolinguistics, linguistics, semantics, discourse analysis, translation, interpretation
Cite this article as:
Volkova, T. A. (2018). Discourse And Communication Translation Model: Designing A Translation Experiment. In & I. V. Denisova (Ed.), Word, Utterance, Text: Cognitive, Pragmatic and Cultural Aspects, vol 39. European Proceedings of Social and Behavioural Sciences (pp. 697-702). Future Academy. https://doi.org/10.15405/epsbs.2018.04.02.100