In this section, I will focus on the difficulties of simultaneous interpreting. As mentioned in Gile’s Effort Model, each Effort demands resources to work properly

In this section, I will focus on the difficulties of simultaneous interpreting. As mentioned in Gile’s Effort Model, each Effort demands resources to work properly. There are different kinds of operations that could make processing demands exceed the available capacity, leading to deterioration in the content and/or the form of the interpretation. As interpreters generally work at the limits of their cognitive capacities (Gile, 1999), these information overflows are common. In this study, I decided to work on different items of the English language that, in my opinion, may be difficult for interpreters working from English into French. These items can be found in different languages even though some of them are specific to English, and are the following: numbers, proper names, complex noun phrases, culture-specific items, idioms, phrasal verbs and single-word terms. Each subsection will provide an analysis that describes the potential difficulties of this type of items and suggest some strategies to cope with those difficulties.
Numbers. In everyday practice, conference interpreters admit having some difficulties with numbers, with error rates reaching 40% for professional interpreters and 40% to 70% for trainees (Korpal, 2016; Korpal & Stachowiak, 2018; Timarová, 2012 in Collard & Defrancq, 2018). It can be surprising that numbers are such a source of problems as their main characteristic is univocity of meaning (Alessandrini, 1990). Their interpretation should be quite easy. In most modern languages, the system developed to create numbers makes it possible to create all numbers using a small set of basic words and fixed syntactic rules, each providing a single piece of information on the number. For example, in English, the main constituents are called base digits (0 to 9 in the decimal system), which can be used to creates two lexical classes: teens (eleven, twelve... nineteen) and tens (twenty, thirty... ninety). There is a fourth set of items, called multipliers, (hundred, thousand) used to mark the magnitude of the base digits. This system very often requires a lot of items and words to create a large number in the target language and therefore the problem encountered by interpreters can be a memory problem due to the need to remember many words for the expression of a single concept (Mazza, 2001). Numbers are thus characterized by high information density and also low predictability, which requires the interpret to change strategies with respect to listening, memory and production (De Smet, Vandierendonck & Defrancq, 2018). Even though interpreters broadly agree that numbers are particularly difficult to interpret, research on the topic is rather limited (Mead 2015: 287). If we look at studies on numbers, Gile mentions them several times in his works (1995, 1999) and highlights the difficulty of numbers in SI because of three different characteristics, which are mentioned by many other researchers: 1) Low predictability (Braun and Clarici, 1996; Mazza, 2001; Pinochi, 2009, Mead, 2015): it increases the Listening and WM Efforts because the anticipation is rarely possible (Jones, 2002; Pinochi, 2009). The number expressed can only be understood once the speaker utters it, and anticipation is therefore almost impossible. 2) Low redundancy: it also increases the Listening and WM Efforts because it is really important not to miss or forget them as they cannot be inferred from any element in the text (Gile, 1995: 108). 3) High informative content (Alessandrini, 1990): sections with numbers increase processing capacity demands for all Efforts as the interpreter must process, remember and translate more information. These characteristics can explain the frequency of mistakes with numbers in SI, especially large numbers which are expressed using several words and can also explain why interpreters cannot use strategies such as paraphrasing or reformulation (Jones, 2002; Pinochi, 2009) as every component of a number is a unit that represents one meaning only. Some strategies can, however, be applied to reduce the number of errors linked to numbers. One possible solution to deal with numbers is the reduction of decalage, i.e. a shorter ear-voice span (EVS)1, which allows shorter retention in STM but faster restitution of the input and also allows the interpreter to stick as close as possible to the speaker. It is suggested that professional interpreters perform better than students when dealing with numbers because they keep a shorter EVS (Timarová et al., 2014). Gile (2008) finds this strategy logical because a short EVS means that the interpreter saves working memory capacity. Kalina (1992: 254) suggests another strategy which is approximation. It can be a useful strategy to deal with particularly complex segments. She thinks this strategy can be used to provide partial information until the interpreter finds a more accurate translation, but this might be counterproductive for numbers as strategies to gain time increase the WM Effort. Finally, since the main problem with numbers seem to be insufficient WM, writing the numbers down while interpreting might also be a good strategy (Mead, 2015; Setton, 1999). Despite the fact that note-taking can represent another Effort which will want its share of the total attentional capacity, it might relieve pressure on WM. A few studies (Crevatin, 1991; Braun & Clarici, 1996) have focused on the management of numerals in SI and the usefulness of note-taking. They showed that notes were useful for numbers during the interpretation into the A-language even though the performance of professional interpreters and students was relatively poor. Furthermore, the language pair was not the same as in this dissertation, namely Italian-English or Italian-German for the two studies mentioned. However, Mazza carried out another study (2001), which showed that taking notes when dealing with large numbers (four digits or more) did not help the interpreter because large numbers are often too dense to be rightly noted.