Local cognitive load in simultaneous interpreting and its implications
for empirical research
Daniel Gile
ESIT - Université Paris 3 Sorbonne Nouvelle
Published in Forum 6 :2.59-77.
Abstract
Investigation of cognitive load-related limitations in simultaneous interpreting can benefit from local analyses as opposed to overall
analysis of speeches. In such local analyses, cognitive load imported from the unfinished processing of the previous segment can be
a determinant of the interpreting difficulty of the current segment – and explain language-specific interpreting difficulties which are
not manifest in everyday conversation. Other factors include information-density distribution in the sentence and inter-sentence
pauses. Manipulation of inter-sentence pauses and a focus on local segments may increase the power of empirical research on
interpreting.
Résumé
La recherche sur les limites de la simultanée dues à la charge cognitive gagne à être menée par la voie d’analyses locales.
Localement, la charge cognitive importée d’un segment dont le traitement n’est pas terminé peut déterminer la difficulté
d’interprétation du segment en cours de traitement, et expliquer des difficultés spécifiques par langue qui ne sont pas manifestes dans
la conversation quotidienne. Interviennent aussi la distribution de la densité d’information dans la phrase et les pauses entre les
phrases. La manipulation expérimentale de ces dernières et une focalisation de la recherche sur des segments locaux pourraient
augmenter l’efficacité de la recherche empirique sur l’interprétation.
Keywords: Effort Models, Imported cognitive load, Local processing, Information density, sentences, empirical
research
1. Introduction
The literature on simultaneous interpreting shows that early on, cognitive constraints were recognized as one of
the most critical limiting factors in simultaneous interpreting performance. This idea can be found in Fukuii &
Asano (1961), Oléron & Nanpon (1965), Kade & Cartellieri (1971), Kirchoff (1976), in Moser’s (1978) model
of simultaneous interpreting, in Chernov’s views on the simultaneous interpreting process (1994) and in many
comments by other authors. In the early 1980s, it was formalized as a central factor of interpreting difficulty in
Gile’s Effort Models (see later in this paper), which have subsequently been used as explanatory models and
didactic tools, but also as a conceptual framework for empirical research. Studies by Guiducci (2002), Petite
(2005), Vik-Tuovinen (2005), Gumul (2006), Soler Caamaño (2006), Wang Xiao Ying (2006), Wang Ying
(2006), Yang (2006), Chang & Shallert (2007) and He (2007) are a few recent examples of such work.
Authors tend to take as analysis units either whole speeches (as adlibbed or read, informationally dense or not,
delivered rapidly or less rapidly etc.) or specific problem triggers (numbers, names, enumerations, idioms etc.).
Interestingly, field observation shows that speeches sometimes turn out to be less difficult or more difficult than
could be expected on the basis of overall parameters, though the relevance of the latter has not been challenged.
This suggests that they may not be sufficient as interpreting difficulty predictors.
This theoretical paper starts with the idea that ‘local’ analysis, i.e. analysis focused on short segments and
sequences of two or three neighbouring segments as opposed to general features of speeches, may provide
explanations which overall analysis fails to uncover. It raises a few points around this idea and discusses their
implications for empirical research on simultaneous interpreting.
2. The Effort Model of Simultaneous Interpreting: a reminder and critical comments
The Effort Model of simultaneous interpreting is a cognitive framework. It conceptualizes SI as a set of
multiple cognitive operations which can be grouped into three ‘Efforts’:
- Online operations which are mobilized to allow ‘comprehension’ of the source speech by the interpreter - the
extent of such ‘comprehension’ may vary. These operations are collectively called ‘Listening Effort’ or
‘Listening and Analysis Effort’.
- Online operations which concur to produce a target speech, including self-monitoring and self-correction. They
are collectively called ‘Production Effort’.
- Online operations which manage in the very short term (up to a few seconds) the storage and retrieval of
information related to the source and target speech in short term memory. They are collectively called ‘Memory
Effort’, a concept distinct from but in many ways similar to the cognitive psychologists’ working memory
model(s).
On top of these three core Efforts comes the Coordination Effort, which manages attention allocation and shifts
between the three. Again, it is distinct from but perhaps comparable to what Baddeley and Hitch have called the
‘Central Executive’ in their Working Memory model (Baddeley & Hitch 1974).
There is no assumption that cognitive components operating in one of the three core Efforts cannot be present
in another. In particular, working memory necessarily comes in both during the Listening Effort and the
Production Effort. However, it is assumed that:
- each Effort includes non-automatic components (which require processing time and processing capacity)
- the combination of any two Efforts and of the three Efforts at a given time generally requires more
processing capacity than any single Effort
Moreover, it is assumed that simultaneous interpreters tend to work close to saturation, in other words that total
processing requirements during interpreting take up most of the interpreter’s available processing capacity – this
‘Tightrope Hypothesis’ (Gile 1999) is the (often implicit) basis of much use of the Effort Models in the literature
and of empirical research around them:
Simultaneous interpreters report regularly that they frequently find themselves unable to understand a sourcespeech segment because their attention is occupied elsewhere (for instance in target-speech production), or that
they are unable to produce an appropriate target-language speech segment while listening to the incoming source
language speech, and that they frequently find themselves in a situation where they have understood a sourcespeech segment but have ‘forgotten’ it by the time they are about to reformulate it in the target speech.
On the basis of such common, uncontroversial albeit anecdotal evidence, it is also assumed that not only are
total processing capacity requirements often close to the maximum available capacity, but simultaneous
interpreters tend to also work close to saturation with respect to each Effort, i.e. that at any time, for at least one
of the three core Efforts, the processing capacity required for the task it is performing tends to be very close to
the capacity made available for it.
These considerations have the following implications:
1. Simultaneous interpreters are vulnerable to conditions where total processing capacity requirements are high,
be it for a whole speech or for a given speech segment.
Such conditions may occur when speeches are dense, fast, spoken with an accent or a type of logic with which
the interpreter is not familiar, when they contain multi-word names or unfamiliar names, numbers, enumerations
etc. - see for example the analysis in Gile 1995, as well as empirical studies on various ‘triggers’ such as
numbers, names and idiomatic expressions (Mazza 2000, Puková 2006, Cattaneo 2004 etc.).
When such conditions occur, they may cause overall saturation or saturation in one of the Efforts; this may
result in errors, omissions or a loss of linguistic and/or delivery quality in the target speech.
2. Simultaneous interpreters are also vulnerable to errors in processing capacity management, i.e. sub-optimal
distribution of attention between the Listening Effort, the Memory Effort and the Production Effort. Such errors
can also produce loss of interpreting quality.
Some Translation Studies scholars lament that the Effort Models (there are Effort Models for simultaneous,
consecutive and sight translation – see Gile 1995) have not been ‘tested’. As mentioned earlier, the Effort
Models are not a theory, but a conceptual framework. Conceptual frameworks can be tested for convenience,
clarity, didactic usefulness, compatibility with existing theories, but falsification tests in the Popperian sense do
not apply since conceptual frameworks do not make predictions. What could be tested are two assumptions
associated with the Effort Models, namely that each combination of two or three Efforts requires more
processing capacity than any single Effort and that interpreters work close to saturation. Regarding these two
assumptions, the following could be pointed out:
- Anecdotal evidence from the field is massive and many authors say, directly or implicitly, that indeed,
interpreters often work close to saturation in at least one Effort and lose information or quality in speech
production when attention management is suboptimal.
- What does need to be tested is the Tightrope hypothesis, as loss of information and/or quality could perhaps be
attributed to processing capacity management errors as opposed to an insufficient total amount of available
processing capacity.
Gile (1999) attempted to generate evidence supporting or falsifying the Tightrope Hypothesis. He asked 10
interpreters to interpret simultaneously the same speech twice in a row and examined errors and omissions. It
turned out that not only did the 10 subjects interpret erroneously different segments, suggesting that it was a
factor other than their intrinsic difficulty which caused the errors and omissions, but when they interpreted the
same speech a second time, some of them interpreted erroneously segments which they had interpreted correctly
the first time. This experiment was replicated by Matisiak (2001), who reports similar findings. Gile argued that
the reason for misinterpretation or omission could not be the subjects’ inability to understand and/or reformulate
said segments and that these findings supported the Tightrope Hypothesis. However, though the evidence
supports the hypothesis of cognitive saturation, it does not necessarily show that such saturation occurs at global
level. It could also be explained by sub-optimal management of processing capacity and a resulting deficit in one
of the Efforts without a deficit in the total available processing capacity. It is not unreasonable to assume that in
experienced interpreters, such local saturation should not occur frequently if available processing capacity is
sufficient for all the Efforts, but the possibility cannot be ruled out.
The following discussion will remain theoretical. It relies on a simple conceptual framework and on much
converging anecdotal evidence, but no attempt at falsification of ideas formulated below in the Popperian sense
has been made at this point.
3. A local view of cognitive load in simultaneous interpreting
The Effort Model of simultaneous interpreting applies holistically to speeches which can be characterized by a
number of factors such as speed of delivery, information density, quality of the speaker’s voice, prosody, accent,
the number of technical terms, the number of names, the clarity of the underlying logic etc. However, regardless
of the overall features of a speech, at local level, around a clause, a sentence or a small group of sentences,
variability may be high and result in the interpreter finding segments in a speech which could be expected to be
difficult easy to interpret or vice-versa.
3.1 The sentence as a local unit of analysis
While speeches and statements taken globally may serve one ‘message’ and express a ‘meaning’ or a ‘sense’, if
they are made up of more than one sentence, they serve this message, meaning, or sense through a sequential
construction process: information and ideas are presented to the listener or reader one after the other to gradually
construct the desired information, message and/or effect. For the present cognitive analysis, I choose to take
sentences as the building blocks being assembled gradually to generate such information, message and/or effect.
In some cases, grammatical sentences consist of several independent clauses, each of which represents a selfcontained idea; it may then make more sense to choose such clauses as the basic local unit of analysis.
For instance, in the following extract from Barack Obama’s speech in Berlin in July 24, 2008,
“As we speak, cars in Boston and factories in Beijing are melting the ice caps in the Arctic, shrinking coastlines
in the Atlantic, and bringing drought to farms from Kansas to Kenya.” after the introductory “as we speak”, there
are clearly 3 such clauses.
In other cases, the very definition of a sentence is not necessarily clear, for instance in many Japanese clauses
connected by “no desu ga”. Nevertheless, I will assume here on the basis of experience and dozens of random
verifications that in interpreted speeches, sentences as defined by traditional grammar make up close to all cases
of verbal expression of autonomous ideas. More importantly, random verifications which I have carried out
using my corpora of original speeches and their interpreted versions collected in the course of empirical studies
on interpreting over the years suggest that in the vast majority of cases, source-speech sentences are converted
into target-speech sentences with similar subject-predicate structures, though their grammatical form may differ.
The sentence therefore seems to be a reasonable and convenient unit for local analysis of interpreting difficulties,
and has indeed been used as such by many authors in the literature.
Another unit for local analysis used by authors who follow cognitive psychology methods is the proposition,
mostly for the purpose of assessing fidelity under different experimental conditions (see for example Tommola
& Lakso 1997, Chang 2005). The proposition is not suitable for the following discussion which focuses on the
influence of surface form and information density along the text, because it is based on the underlying logical
structure of the text and does not take into account the location of various information elements in the sentence.
3.2 Cognitive load at sentence level
3.2.1. Imported load, current load and exported load
If one excludes the first sentence of a speech or a sentence produced after a relatively long break (more than a
few seconds), cognitive load for the simultaneous interpreter who processes a sentence is produced partly by the
processing of the sentence itself and partly by the processing of the previous sentence, since EVS (ear-voice span)
is of the order of one to several seconds; unless the interpreter managed to anticipate the end of the previous
sentence and finish interpreting it before or right at the time the speaker finished uttering it, s/he will have to
complete its processing after the new sentence has started. In concrete terms, while listening to the beginning of
a speaker’s new sentence, the interpreter may still need to retrieve the last part of the previous sentence from
short-term memory, decide how to reformulate it in the target language or, if this is already done, utter its targetlanguage version while monitoring his/her own output. These tasks which come on top of the processing of the
new sentence produce ‘imported cognitive load’.
Again, Obama’s speech in Berlin provides a good example:
“Sixty years ago, the planes that flew over Berlin did not drop bombs; instead they delivered food, and coal, and
candy to grateful children. And in that show of solidarity, those pilots won more than a military victory.”
When I interpreted the speech, in this sentence which talked about the Berlin airlift, I could anticipate “food”
on the basis of existing background knowledge, but had forgotten about coal and did not expect the reference to
candy. In particular, I had to examine the likelihood of having actually heard “candy” along with food and coal.
This was confirmed when I heard “children”, the very last word of the sentence. The last part of the sentence was
reproduced as “et des bonbons à des enfants reconnaissants” while the next source-speech sentence had already
started. This overlapping of 7 target-speech words (and 12 syllables) necessarily took some processing capacity
away from the attention available for comprehension of the first words of the next sentence. Note that the first
meaningful word group in the first sentence “And in that show of solidarity” has only 6 words and 10 syllables,
and may well have been entirely processed under this condition of partial availability of attention because of
imported load.
Also note that imported cognitive load only represents the ‘liability’ side of the sequential processing of
sentences in a speech; the ‘asset’ side is not only important, but generally essential. The processing of any part of
a speech is generally facilitated to a considerable extent by upstream processing of the previous parts, as they
provide the context which makes comprehension easier and faster through gradual construction of a mental
model (see Setton 1999). In the example presented above, the first sentence makes it easier to understand
“solidarity” and “more than a military victory” in the next sentence. Thus, overall, interpretation of one sentence
reduces processing capacity requirements for the Listening Effort and perhaps the Production Effort in the next
sentence.
However, in spite of this facilitation effect, cognitive saturation occurs frequently. This justifies a closer look at
the effect of time lag in completing the processing of one sentence on the processing of the next – an effect
which may well have significant implications on interpreting quality.
3.2.2. Local effects of imported cognitive load
a. The importance of local considerations
One obvious implication of the addition of imported load to current load is that cognitive saturation, both at
overall level and at the level of individual Efforts, depends not only on the sentence being processed, but also on
the previous sentence. In a process similar to the one explained in Gile 1995 (chapter 7) for non-immediate
effects of triggers within a sentence, the addition of imported load and current load increases the likelihood of
failures even when the current sentence is rather easy to process. This idea is supported inter alia by the findings
of Lee (2006), who also used sentences as units of analysis and noted that when too much time was spent on one
sentence, this had a deleterious effect on the processing of the next.
At first sight, it might appear that such failures are likely to occur mostly at the beginning of the new sentence,
while the interpreter is completing the processing of the previous sentence. However, interpreters may also
decide to devote more attention to the listening component while processing the beginning of the new sentence
in order not to miss important information. This may result in successful processing of the beginning of the new
sentence, but at the expense of Production and/or Memory, which can result in extra cognitive load carried over
into the processing of downstream segments; the associated knock-on effect may lead to saturation at a later
stage, perhaps towards the middle or even the end of the new sentence, in a cascade of near-saturation events
(Gile 1995, chapter 7). Failures not in problem triggers, but around them, have been observed in empirical
research, for instance by Cattaneo (2004) and Puková (2006).
b. The importance of information density distribution within sentences
Another obvious implication of imported cognitive load is that beyond general features of a speech such as
speed, level of technicality, voice quality, accent etc., the specific distribution of information density along
single sentences can determine interpreting difficulty to a considerable extent: depending on where and how
information is distributed in a sentence, it may export a smaller or larger load into the next sentence.
In particular, sentences with high information density towards the end are more likely to export cognitive load
into the processing of the following sentences than sentences which end with little information or information
which is easy to anticipate. In this respect, notwithstanding the existence of pragmatic markers (Setton 1999) and
of anticipation possibilities based on the interpreter’s knowledge of the communication context and the speaker’s
intentions, language-specific differences may be significant. For instance, Japanese speeches tend to include
many sentences with highly predictable sentence endings of six, seven or more syllables (see Gile 1992), which
could reduce markedly the cognitive load exported into the processing of the next sentences. In many cases, the
final verb in German sentences is also predictable, but it is much shorter and does not afford the same cognitive
relief.
c. Language-specific interpreting difficulty
Conversely, for obvious reasons, sentences with informationally dense beginnings are particularly vulnerable to
imported cognitive load. So are sentences with embedded structures. Embedded structures are found in written
texts more than in adlibbed speeches, and in some languages (such as German and Japanese) more than in other
languages (such as English and French). At local level, as demonstrated in many studies by cognitive
psychologists (see for instance papers in MacDonald 1997) syntactic features of language may have significant
implications in terms of cognitive load during comprehension. Proponents of ESIT’s ‘Interpretive Theory’
argued against the idea that interpreting difficulty was language-specific (see for example Déjean Le Féal 2002,
page 145). Seleskovitch (1981, page 40), went as far as to say it is absurd to claim that German sentences cannot
be understood as rapidly as French sentences because they contain embedded structure or place the verb or the
indication for a negative at their end. Assuming she is right globally, at local level, within a sentence, it seems
undeniable that depending on syntax, at least some of the pieces of information with which global meaning is
constructed do not arrive in the same order, which, in view of short-term memory limitations and the Tightrope
Hypothesis, does have implications on the interpreter’s options in target-speech production. This is where the
comprehension parameters differ in interpreting and in everyday communication, where people can wait until a
sentence is finished before reacting. This is also why language-specific factors which may have no significant
effects in most other communication settings could make a real difference in interpreting.
d. Pauses
Imported cognitive load is reduced when there are pauses between sentences, whether rhetorical or due to
hesitations. This time interval with no information input allows the interpreter to focus on retrieval of
information from memory and/or production with no need to allocate part of his/her processing capacity to the
analysis of incoming speech. Thus, informationally dense speeches with relatively long inter-sentence pauses can
be easier to interpret than speeches with lower density but shorter pauses. As to pauses within sentences, they
can provide interpreters with considerable cognitive relief if they come in at a time when a full logical
proposition has been expressed, but with little relief if they are located within a proposition.
For instance, the July 2008 Obama speech in Berlin contained the following sentence:
“As we speak, cars in Boston and factories in Beijing are melting the ice caps in the Arctic, shrinking coastlines
in the Atlantic, and bringing drought to farms from Kansas to Kenya”
Pauses after “Arctic”, “Atlantic” and “Kenya” could provide cognitive relief because they allow the interpreter
to finish processing a clause or most of it before starting to process the next. Pauses before “Boston”, before
“Beijing”, before “Atlantic” and before “Kenya” would not be very useful to the interpreter, who would have to
hold information from the same proposition in working memory until the missing information came in so that it
could be reformulated in the target speech.
e. Sentence length
Sentence length may also determine interpreting difficulty at local level to a significant extent. A long sentence
may deny the interpreter relief-affording pauses over a relatively long stretch of speech, and is particularly
difficult to interpret if it involves embedded structures which require him/her to keep much information in shortterm memory. If however it is composed of independent clauses and the speaker pauses after each for effect, as
happens inter alia in many political speeches (see the example above), cognitive relief is provided and sentence
length may not be correlated with increased difficulty.
Incidentally, because of the interpreter’s use of the speaker’s pauses to catch up with him/her, rhetorical pauses
may disappear in the target speech and perhaps reduce the desired rhetorical effect in political and other speeches
where such effects are important. This is one aspect of interpreting quality which has failed to attract the
attention of investigators so far.
4. General implications
4.1 Local analysis and overall analysis
It therefore appears that for cognitive analysis, it makes sense to look at the interpreting process on both the
local level and a more general level (from a single full statement or speech and up to the background and stakes
of the conference). The latter can be viewed as the ‘context’, partly given by pre-existing knowledge about the
meeting, its actors and the stakes, and partly taking on shape and substance by information collected from the
exchanges as they are being interpreted. Local analysis can focus on a single sentence or on a single independent
clause with its immediate neighbours (its direct predecessor and successor) when considering imported and
exported cognitive load respectively.
As mentioned previously, theoretical analyses and much anecdotal evidence make it clear that the context (at
overall level) is important for local processing, in particular in terms of ease of language perception,
disambiguation and anticipation - though Anderson (1979) failed to show significant differences in the
performance of interpreters who had previous knowledge of the content of the speech as opposed to those who
did not. However, specific interpreting failures linked to cognitive saturation are probably best examined at local
level (see an example in Laplace 2002).
4.2 Variability in local processing tactics and implications on overall performance
The success of interpreting in a particular speech depends not only on the interpreter’s general level of relevant
knowledge and his/her skills, but also on local tactics, i.e. on his/her decisions at every point in the speech.
Deciding to follow the general sentence structure of the source speech in the target-speech sentence or to reorganize the sentence, to seek to retrieve the ideal word from long term memory at a time where it is not highly
available or use a more available but less elegant or accurate word, to convert a large number in Chinese into
English and translate it fully or just indicate its general order of magnitude, any such local decision may have
significant implications on cognitive load and determine success or failure in the interpretation of specific
sentences.
Local decisions made online by the same interpreter can change from one segment to the next. There may also
be high intra-individual variability in repeat interpreting of the same speech. As an interpreter, I have sometimes
re-interpreted the same speech more than once, often when interpreting for recorded TV programs due to be
broadcast later and ‘cleaning up’ an initial interpretation for smoother rendition on the air. When interpreting the
same speech a second time, I have often noted that my decisions on how to handle a particular sentence changed
and resulted in different outputs, sometimes with clumsy or incorrect rendition of parts of the sentence which
had been interpreted better the first time. This is also what seems to have happened in the re-interpreting
experiment reported in Gile (1999), which provided data from 10 professional interpreters.
If the tightrope hypothesis is true, such variability may have significant implications on local saturation and on
local errors and omissions, though it may be smoothed out in overall quality as measured over the whole speech.
At this point, it is only possible to speculate, but simple experiments with repeat interpretation could be
informative in this respect. If intra-subject variability does indeed turn out to be of marked amplitude, this would
have to be taken into account when assessing experimental findings on the effect of various input variables on
interpreting: the lack of significant differences could be due to noise from variability in local processing, and the
existence of significant differences could be interpreted as a indicating a certain magnitude of the effect –
provided samples are large enough and findings are confirmed by multiple replications.
4.3 Sentence structure and inter-segment interface
Two aspects of local phenomena need to be addressed, one being processing tactics (see above) and the other
local structures in the source speech. In the latter, besides the presence of features requiring higher processing
capacity and problem triggers (including high delivery speed, unusual accent, monotonous or unusual intonation,
faulty grammar, homophones, rare words, enumerations etc.), an important parameter is the interface between
succeeding sentences, i.e. inter-sentence pauses, sentence endings and sentence beginnings. As suggested earlier,
these may be strong determinants of performance, along with overall source-speech parameters.
An implication of their importance is that in experimental research, manipulating sentence endings (for instance
by adding longer predictable sentence endings in Japanese source speeches) and/or sentence beginnings by
introducing place-holding fillers (such as “as I was saying, Mister Chairman”) and/or introducing inter-sentence
pauses can help isolate the effect of specific problem triggers from the noise caused by imported load. Such
manipulations may give hypothesis-testing experiments higher sensitivity while maintaining reasonable
ecological validity, something which construction of sentences with specific syntactic and/or propositional
features (see for instance Dillinger 1989) often fails to do. This might be a simple but powerful experimental tool
in view of potential high inter-subject and intra-subject variability in instantaneous sentence-processing.
5. Summary and conclusion
In this paper, I have argued on the basis of the Effort Model of simultaneous interpreting and the Tightrope
Hypothesis that analyzing interpreting output at local level (roughly at the level of independent clauses or
sentences) may yield more insights into interpreting difficulties than considering overall features of speeches. I
have also stressed that ‘imported cognitive load’ may have a strong effect on interpreting performance and
suggested that pauses and sentence endings with low information density can reduce such interference.
In terms of empirical studies into interpreting, this suggests inter alia the following avenues for research:
- Firstly, the relevance of imported cognitive load needs to be tested and perhaps quantified, at least roughly.
This could be done experimentally by manipulating inter-sentence pauses (by lengthening unfilled pauses or
introducing filler sentences and expressions) and checking the effect on performance.
- Secondly, in experimental studies, comparing performance on potentially problematic local segments having
the targeted features (i.e. those associated with a particular independent variable) as opposed to overall
performance on speeches might increase the discriminatory power of the comparison: those parts of the speech
where most interpreters are not likely to encounter difficulties may be less revealing, and ignoring them in the
analysis could make interpreting performance analysis more sensitive.
- Thirdly, in such experimental studies, in order to reduce noise from imported cognitive load, source speeches
could be manipulated by lengthening inter-sentence pauses and/or by adding plausible fillers.
- Fourthly, it seems useful to work at identifying more specifically local problem triggers other than those which
are already known from the literature (enumerations, numbers, multi-word names etc.). This includes in
particular language-specific sentence structures. When such structures have been identified, it may be easier to
assess interpreting difficulty of various speeches and speech segments and use such knowledge for further
research and perhaps for training through recommendations to students.
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Examples are cited from Barack Obama’s speech in Berlin, July 24, 2008.
A raw transcript can be found at the address: www. elections.foxnews.com/2008/07/24/raw-data-transcriptof-obamas-speech-in-berlin