In an attempt to define the notion of translation universals, Toury (2004) requests the formulation of conditioned statements which would provide “probabilistic explanations in translation studies”. Conditioned statements would predict “par-ticular modes of behavior (or their observable results) … [based on] an array of variables, whose capacity to enhance (or reduce) the adoption or avoidance of a particular behavior would be verified empirically” (Toury 2004: 24). The most general format of such a conditioned statement according to Toury would be as
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follows:
If 1 and 2, and 3, and …∞, then there is great likelihood that X […] where the numbers (1, 2, 3, …∞) stand for the different variables which may have an effect on the selection of a translational behavior. (p. 26)
In terms of the notion introduced above, Toury’s conditioned translation state-ment can thus equivalently be expressed as a set of conditional probabilities in the form P(xi|v1,v2, . . .,vm) where xi stands for a particular predicted transla-tion behavior and the set V = {v1,v2, ...,vm} contains predictor variables which have an effect on and explain — to a certain extent — the observed behaviour xi
in a probabilistic manner. Even though there might be many different modes of translation, we assume that the translation process consists of a finite inventory of behavioral patterns X = {x1, ..., xn} and we assume — in contrast to Toury — that for each of the observations xi, 1≤i≤n there exists only a finite set of predic-tor variables vj ∈ V that has an effect on xi. The observed translation process X can then be formalized as a sequence of possible behavioral patterns x1, x2. . . that are conditioned by a number of predictor variables v1,v2. . . . The most likely (explanation for) translation behaviour bX can thus be computed in a similar way as the most likely translation cTT.
The general idea in this model is that the value of a dependent variable (X ) is related to a set of independent variables (V ) through a function (F ). Given the translation UAD, we learn the function (F ) to minimize the error (also known as loss (L)) in prediction (bX) of the variable V .
minimize L(X, bX), where bX = F(X,V ) (4.5) 2.2 Latent translation states
As an illustration of a probabilistic conditioned statement, Toury (2004) discusses a made-up example in which he illustrates a hypothetical effect of experience and fatigue on whether translational processing will be applied to small or low-level textual-linguistic entities. In this example, the level of textual-linguistic entities that a translator works with would be represented by the dependent variable X whereas the explanatory (or predictor) variables V represent the experience and fatigue of the translator which may have an effect on the choice of the translation unit.
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4 Sketch of a Noisy Channel Model for the translation process
Toury is not very consistent in his usage of the terms “translation modes” and
“translation behavior”. Surprisingly, for him the translator’s behavior “is not re-ally observable in any direct way” (Toury 2004: 26). He nevertheless mentions possible forms of translational behavior which include all kinds of “regularities [that] can be found on every level, from the individual act of translation […] to the overall notion of translation”, under which he also subsumes translation uni-versals, i.e. structures in the translation product. We will come back to this issue in the conclusion.
On the one hand, Bernardini (2001: 241) points out, “an understanding of trans-lation [processes] […] is not derivable solely from an analysis of the final prod-uct”. On the other hand keylogging and eye-tracking technologies give us today the possibility to directly observe and investigate translation behavior and em-pirically assess the granularity of the chunks that a translator works with. Ac-cordingly, we conceptualize the translation process as successive intermediate versions of a text (i.e. the emerging translation), which are the direct conse-quences of translation behavior. Most important in this process are obviously the keystrokes which are the direct causes for text modifications.
As an extension to Toury’s model, we assume that the behavioral patterns are triggered through internal (latent) states in the translator’s “black-box”. Using EEG and fMRI technologies we may be able to investigate and measure these la-tent states directly through experimental equipment in the near future (Annoni et al. 2012). Currently however, Think Aloud Protocols (TAPs) and introspection are methods used to assess the hidden (or latent) states in the translation process.
Two of the main goals of TAP research are (1) to describe translation problems, and (2) to isolate strategies and translation procedures. According to Lörscher (2005: 599), the “data [that are collected through TAP] are interpreted as (observ-able) indicators of (unobservable, mental) translation strategies” which, for him, represent the basis for the formation of hypotheses regarding the mental trans-lation process. Based on the collected data, Lörscher (1991) describes five basic translator types which differ with respect to how much the solution of a trans-lation problem is automatized, whether the translator requires search, whether a translation problem is decomposed into smaller parts, and to what extent the translation problems are consciously accessible and can be verbalized. Lörscher (1991: 280) finds that “[w]hen several [translators] are faced with a problem X, many or most of them employ similar or the same types of strategy”.2 How-ever, findings like these remain to be quantified and scrutinized for their
predic-2While this looks similar to Toury’s conditioned statement, the “problem X” would here be a predictor variable, while the dependent variable “types of strategy” is a latent translation state.
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tive value. For Krings (1986), TAPs have only a restricted validity. He cautions that “although verbal data do give evidence of mental processes, they cannot be claimed to be isomorphic with those processes” (Krings 1986: 264).
Rain Dry
Low High
0.6 0.4 0.4 0.6
0.7
0.3 0.2 0.8
Figure 2: A hidden Markov Model
The translation model suggested by TAP analysis can be formalized as a Hid-den Markov Process in which a number of interconnected “hidHid-den” states emit observations with a certain probability. Figure 2 outlines the idea of a hidden Markov model, where the two hidden states (Low and High) emit possible ob-servations (Rain or Dry) with a certain probability. A hidden Markov model can consist of a large number of hidden states and emit many different observations.
The hidden states are organized in the form of (possibly completely connected) recursive networks and transition probabilities that indicate the likelihood with which one state follows another. A number of efficient algorithms exist to learn transition and emission probabilities from data and to compute most likely se-quences of observations.