Towards a Model of Dialogue Interpreting

Early models of interpreting—process-focused and otherwise—often conceptualized

interpreting as a mechanistic process in which interpreters were (ideally) a passive and invisible medium through which information passed (Wadensjö, 1998; Roy, 1993, 2000; Wilcox &

Shaffer, 2005). The view of the interpreter as conduit is reflected in common metaphors for interpreters/interpreting, such as those listed by Roy (2000:101): “a machine, a window, a bridge, and a telephone line.” Wadensjö (1998:7) refers to this as a “transfer model of communication,” in which meaning is understood to arise solely from the speaker, and the successful 'receipt' of that meaning by the message recipient is seen as a simple, straightforward fact.

This view is based on a number of epistemological assumptions about language and

communication: that communication is an uncomplicated, straightforward process; that meaning is 'in' language (that is, the meaning encoded in language is transparent and objective); and that messages are 'sent' by a speaker directly 'to' the intended 'receiver' (listener) (Mason, 1999;

Wilcox & Shaffer, 2005). This understanding of communication has been widely questioned in the literature (Wadensjö, 1998; Roy, 2000; Wilcox & Shaffer, 2005; Shaffer, 2013). Mason (1999:150) notes that such “mistaken assumptions” about language and communication are problematic for interpreters in that they lead to a simplistic understanding of interpreting as well as to unreasonable expectations on the part of clients, such as requests for 'verbatim' or 'literal' translation. Wilcox & Shaffer (2005) argue persuasively that these assumptions about language and communication run counter to current scholarly consensus around the fundamentally dialogic nature of communication.

A dialogic understanding of communication views sense-making as a process requiring active involvement of both the speaker and the hearer of a piece of discourse (Wilcox & Shaffer, 2005; Janzen & Shaffer, 2008; Shaffer, 2013). Meaning is not produced or sent, but rather constructed by the recipient on the basis of the speaker's verbal and nonverbal output in concert with context, or, as Wilcox & Shaffer (2005:27) express it, “communication, and therefore interpreting, is an active process of constructing meaning based on evidence provided by

speakers.” Wadensjö (1998:8) also insists on the co-constructedness of meaning as developed in talk: “communication, as well as mis-communication, presupposes a certain reciprocity between the people involved.” That is, meaning cannot be understood as being wholly present in an utterance, such that each utterance is meaningful independent of its context. Rather, sense-making involves both the speaker and the listener and is influenced by context both at the level of the individual speaker and of the unfolding interaction (Kohn & Kalina, 1996; Diriker, 2004;

Janzen & Shaffer, 2008).

The process of sense-making becomes even more complex when interlocutors communicate across linguistic and cultural barriers with the aid of an interpreter (Kohn & Kalina, 1996;

Pöchhacker, 2005; Janzen & Shaffer, 2008). As Wilcox (1986:5) puts it, interpreting is

“the creative process of 'making sense' out of what is happening and expressing this sense to the speaker of another language. The interpreter's skill and effectiveness depend on the extent to which these constructed worlds of sense map on to the worlds of sense which speakers are also constructing and expressing in their language. The creation of sense requires the active working of people's minds.”

It is this communication-related complexity that Rudvin (2006:173) is referring to when she states that “texts are not simply terminological systems, but systems of knowledge and belief.”

In this research I take an emic (Headland, Pike, & Harris, 1990) approach to the object of study, foregrounding the interpreter as task-performer and conceptualizing the task from the interpreter’s perspective. I do so advisedly, however, acknowledging the impossibility of separating the interpreter and his/her processing from the interpreted interaction as a whole.

Indeed, formulating a model of dialogue interpreting that considers the interpreter and his/her processing in isolation from the communicative situation and the parties involved in it would be narrow and reductive (see Englund Dimitrova & Tiselius, 2016, discussed in Section 1.2). Both the interaction itself and the interpreter’s task performance are inevitably influenced not only by the interpreter’s own background, worldview, abilities, and decisions, but also by various characteristics and behaviors of the parties to the interaction and the interplay between them throughout the course of the interaction. A process model of dialogue interpreting must, therefore, take into consideration the presence, influence, and interaction of all of the parties involved.

The quotation from Roy (2000:103) that appears at the beginning of this dissertation

describes the interpreter as “operat[ing] within an emergent system of adaptability.” This view of the interpreted interaction as a system provides a useful point of departure for conceptualizing the entirety of the task. The study of complex systems, as an academic discipline, seeks to better understand systems that are characterized by a number of specific features, including the

following: they tend to feature a number of highly interconnected components that interact with each other in a non-linear fashion; causal links between inputs and outputs are generally not clear-cut; and small differences in starting points or inputs may give rise to quite distinct outcomes (Byrne, 1998; Arrow, et al., 2000). I do not explore complex systems theory in this dissertation, nor do I argue that ‘the interpreted interaction’ meets all of the criteria (or, rather, one of the multiple possible sets of criteria; Ladyman, et al., 2013) to be designated a complex system in the technical sense used in the literature of that discipline. I do propose, however, that conceptualizing the interpreted interaction as a complex system provides a productive

springboard for thinking about the components and features of the interpreted interaction, the interplay between them, the variables influencing the interaction, and the interpreter’s online self-regulation. Such an approach facilitates an integrated approach to understanding dialogue interpreting by situating the interpreter’s processing and task performance firmly within the context in which the interpreted interaction takes place and taking into account the

social/interactional features of the task.

The interpreted-interaction-as-system comes into existence when a minimum of two participants come together to interact with each other in service of some purpose. Since the parties do not share a language in common, they rely on the services of a third person, who speaks both languages and is thus charged with facilitating communication between the parties.

The communicative actions and reactions of each interlocutor are inevitably influenced by a number of personal characteristics such as linguistic/social/cultural background & worldview, personality, communication goals, knowledge of the setting/context, and so forth. The interpreter also brings to the table his/her own prior experience (background, worldview, personality, ideas of professional practice/behavior, etc.), which inevitably influence his/her actions and reactions during performance. The interactional system is also influenced (and, potentially, constrained) by features of the communicative or institutional context in which it takes place, such as a court of law, where communication is stylized and follows largely predetermined patterns

(Berk-Seligson, 2002; Hale, 2004; Pöchhacker, 2005), or a medical setting, which also has genre-specific characteristics and patterns of discourse (Tebble, 1999, 2009; Davidson, 2000; Meyer, 2002). As the system operates—that is, as the interaction progresses—it is further influenced by the ongoing interplay between the interlocutors, as well as between the interpreter and each of the interlocutors. The system of the interpreted interaction thus encompasses and is influenced by a number of factors and variables, some of which are intrinsic to the individuals involved, and others of which arise from the context and the interplay among the parties as the interaction unfolds.

Many of the factors potentially influencing the system as a whole, as well as the interpreter’s performance—such as the interpreter’s background, prior experience, and training; the

interlocutors’ communication goals; and context- or setting-specific constraints—do not lend themselves easily to observation or quantification. Their potential to influence task performance may also be difficult for those unfamiliar with interpreting (including students) to conceptualize.

For these reasons, it may be helpful to draw an analogy between the situation of the interpreter and that of another class of task performers: operators of motor vehicles. While these tasks are superficially dissimilar, they share a number of characteristics, and the variables influencing driving performance are more likely to be observable and/or quantifiable than those that may influence interpreting performance. This point, taken together with the fact that most people are more familiar with the parameters of the driving task and to have direct experience of task performance than is the case with dialogue interpreting, suggests that drawing an analogy

between the two tasks may prove helpful in describing the aims and contribution of this research.

Like interpreters, drivers are faced with a complex, dynamic, and goal-oriented performance task that is influenced by an array of factors. For drivers, these factors include the physical characteristics of the vehicle, potential roadway hazards, weather, and internal and external

distractors. The driver must take all of these factors into account during task performance, and must monitor the current status of and the potential for changes in factors such as the vehicle’s performance (e.g., mechanical difficulties), weather and road conditions (e.g., debris, snow, stopped vehicles), the route s/he is following (e.g., whether it is a known or unfamiliar route) and the state or requirements of the passengers or cargo (e.g., children fighting in the back seat or hazardous chemicals in a tanker), as well as other potential disruptions or distractions (e.g., sleepiness, a ringing cell phone). The driver has recourse to multiple control mechanisms when online monitoring processes indicate they are needed. The range of control mechanisms available and a given driver’s choice from among them are influenced by numerous internal and external factors. Internal factors might include the driver’s level of comfort with the vehicle or road conditions, knowledge of the surrounding area, skill or confidence level with regard to driving in adverse weather conditions; external factors might include the type of vehicle and its cargo (e.g., a family car vs a semi-tractor trailer), current weather conditions, and the goals of the task (e.g., getting a sick person to the hospital vs a pleasure outing).

Like the driver, the interpreter performs a complicated task, monitors a range of variables that may affect performance, and draws on a variety of control mechanisms to respond to problems or avert potential problems. The research undertaken for this dissertation helps to explicate the interpreter’s experience of the interaction-as-system. It provides empirical evidence of the aspects of the interaction-as-system that influence interpreters’ performance in a manner similar to that in which factors such as the weather, the type of vehicle, and the characteristics of the cargo/passengers influence driving performance. It also provides empirical evidence of the range of control mechanisms available to interpreters when monitoring indicates a need to maintain/increase alignment between the current state of the system and their mental representations of the ideal or goal state(s) of the system.

Given the complexity of the system that comprises the interpreted interaction, and the high likelihood that its various components, variables, and processes do affect the interpreter’s

performance, describing the system is a necessary precursor to proposing a model (or models) of the task. The research reported on in this dissertation is an important first step toward modelling the system: it sheds light on salient features and characteristics, variables that may influence interpreters’ performance (and, thus, the system as a whole, inasmuch as the interpreter’s performance is fundamental to the continued functioning of the system), and tools interpreters may employ to achieve/maintain the system’s operations.

5.5 Conclusion

This final chapter of the theoretical portion of the dissertation began and ended with process models of interpreting. The first part of the chapter reviewed the treatment of online monitoring in two process models of simultaneous conference interpreting (Moser 1978, Setton 1999), while the last section discussed the need for a model of dialogue interpreting to reflect (a.)

contemporary understandings of communication, and (b.) the full complexity of the ‘system’ that is the interpreted interaction. The final section also further explicated the goals of the research reported on in this dissertation. The middle sections of the chapter reviewed a number of studies that provide evidence of (simultaneous conference) interpreters’ online self-regulation (Moser, 1978; Ivanova, 1999, 2000; Shlesinger, 2000; Vik-Tuovinen, 2000, 2002; Mead, 2002; Gile, 2009; Tiselius & Jenset, 2011), and drew on the Interpreting Studies literature (Ivanova, 1999, 2000; Hale, 2007; Gile, 2009; Dean & Pollard, 2011, 2012, 2013; Arumí Ribas, 2012; Englund Dimitrova & Tiselius, 2016), experience, and intuition to suggest a range of potential targets for online monitoring and potential online control mechanisms that are specific to dialogue

interpreting.