The Berkeley Aligner and the literal translation hypothesis
Moritz J. Schaeffer, Kevin Paterson, Victoria A. McGowan,
Sarah J. White, Kirsten Malmkjær
University of Leicester, UK
Abstract
This paper presents an eye-tracking study which
compares reading for translation with reading for
comprehension. The number of target words
required for a single source word was manipulated
(one-to-one vs. one-to-many).
One-to-many
alignments resulted in longer first fixation
durations only during reading for translation.
Results are interpreted in terms of the literal
translation hypothesis and it is shown why the
Berkeley aligner can be seen as an implementation
of this hypothesis.
1
Introduction
There is good evidence to suggest that two words
in two different languages which are equivalent
share a semantic representation (e.g. Keatley et
al. 1994; Gollan et al. 1997; Jiang 1999; Jiang &
Forster 2001; Basnight-Brown & Altarriba 2007;
Duyck & Warlop 2009; Duñabeitia et al. 2010;
Schoonbaert et al. 2011). There is also good
evidence to suggest that where syntax in two
languages is similar, it also shares a syntactic
representation (Pickering and Ferreira 2008).
Schaeffer and Carl (2013) report from a study
which used immediate verbatim recall as a
measure of priming. During translation, recall of
the ST was significantly higher than during
reading for comprehension. The higher recall
during translation was interpreted as evidence for
translation equivalent and syntactic priming. Carl
and Schaeffer (2013) also proposed a model on
the basis of the assumption that a relationship
between source text (henceforth ST) and target
text (henceforth TT) can be established in two
ways: either via direct links between items in the
two linguistic systems (horizontal translation) or
via representations regarding propositional
content (vertical translation). Vertical translation
describes a process which consists of
monolingual reading, monolingual writing and
problem solving processes which establish a
relationship between ST and TT in accordance
with contextual considerations. During vertical
translation, the ST is in an abstract form which is
not language specific. The TT is produced on the
basis of these abstract representations. During
horizontal
translation,
higher
level
representations are not necessarily activated, i.e.
ST and TT are not necessarily fully
comprehended and horizontal processes only
take a limited amount of contextual information
into account: equivalence is established at low
levels and between single words or small groups
of words. The model proposed by Schaeffer and
Carl argues that both horizontal and vertical
processes occur in parallel.
2
A potentially universal phenomenon
Tirkkonen-Condit (2004: 183) points at the
“(potentially universal) tendency of the
translating process to proceed literally to a
certain extent”. The study by Tirkkonen-Condit
(2004: 177) compared the occurrence of Finnish
unique items which “…lack straightforward
linguistic counterparts…” in other languages in
corpora of translated and originally produced text
and found that these unique items were more
frequent in original Finnish texts than in
translated Finnish texts. These items were
considered unique, because “…they are simply
not similarly manifested (e.g. lexicalized) in
other languages.” (2004: 177)
on eye movements during reading, it is therefore
necessary to use an eye-tracker with a higher
temporal resolution and to control for factors
known to affect eye movements during reading
in the design of the stimuli.
4
Figure 1: Unique items and their relationship to other
languages (Based on Tirkkonen-Condit 2004)
The evidence Tirkkonen-Condit provides,
suggests that for unique TT items “…there is
nothing in the source text that would trigger them
off as immediate equivalents…” (2004: 183) and
the translator therefore uses a more similar
(literal) expression. This is further supported by a
similar corpus study (Eskola 2004). Malmkjær
(2005) and Tirkkonen-Condit (2004: 182) refer
to Toury’s “law of interference” (1995: 274-279).
Toury posited that “…in translation, phenomena
pertaining to the make-up of the source text tend
to be transferred to the target text…” (1995: 275).
The corpus studies by Tirkkonen-Condit and
Eskola support this.
Malmkjær (2005) argues that the evidence put
forward by Tirkkonen-Condit and Eskola
suggests that the literal translation hypothesis,
and its corollary, the unique items hypothesis and,
by extension, the law of interference might be
cognitively rather than socially determined. This
makes these hypotheses good candidates for a
universal phenomenon which occurs in
translation between all language combinations.
3
Controlling variables
Studies investigating the cognitive processes
during translation rarely control for factors
known to affect eye movements during reading,
such as word length, frequency and predictability
(e.g. Göpferich et al. 2008). In addition, very few
studies employ a control condition which would
make it possible to isolate the effect of the
reading purpose on the cognitive processes
during reading. Most studies investigating eye
movements during translation use an eye-tracker
with a relatively low spatial and temporal
resolution. Typically, an eye-tracker with a
temporal resolution of 50 Hz is used. It is
therefore not possible to investigate effects
below a 20ms threshold. In order to investigate a
complete time course of the effect of translation
Self-paced reading and translation
The studies by Bajo and colleagues (Macizo and
Bajo 2004; Macizo and Bajo 2006; Ruiz et al.
2008) presented stimulus sentences using
masked self-paced reading. During masked selfpaced reading, participants call individual words
onto a computer screen by hitting a key and only
one word at a time is visible on the screen. This
procedure makes it impossible to re-read
previously read words. Bajo and colleagues
compared reaction times during reading for
repetition with reading for oral translation.
Results in all three studies showed that the
instruction to speak a translation had an effect on
reaction times. Bajo and colleagues argue that
during reading for translation, equivalent
representations are activated via direct links to
ST words. These experiments controlled more
factors than most studies which investigate
translation, but they did not test the predicted
TTs: ST sentences were manipulated in different
ways and always predicted how participants
would translate a given ST sentence. However, it
is often possible to translate a given sentence in
different ways. This is of particular importance
regarding the question whether reaction times as
measured in these studies can safely be attributed
to processing of the currently viewed word or
whether the reaction time for a particular word
may involve processing of a different target
word. Self-paced reading times are also typically
much longer than the normal reading times for
text (often twice as long or more). This in part
reflects the time required for the brain to
program the manual response to indicate that the
currently displayed text has been read and to
initiate the display of the next portion of text.
An important consequence of these longer-thannormal reading times is that more than the usual
amount of time is available to engage in
language processing (including processes
associated with translation). However, the
experimental design might have forced
participants to translate the ST word which was
visible on the screen as and when it was visible.
Bajo and colleagues presumably employed this
procedure in order to experimentally separate
two processes which can and often do occur at
the same time: often, translators read and write at
the same time (Carl and Dragsted 2012) or shift
their visual attention between ST and TT
frequently (e.g. Jensen 2011).
It is therefore important to exclude the
possibility that it is the experimental design
which is responsible for the observed effects.
5
Task effects on eye movements
during reading
Kaakinen and Hyönä (2010) carried out a
study
which
compared
reading
for
comprehension with reading for proofreading.
Kaakinen and Hyönä (2010: 1563) report global
task effects in early and late measure on all
words in critical sentences and the analyses on
critical words also showed early and late task
effects. Kaakinen and Hyönä (2010: 1565)
interpret these results as suggesting “…that
readers ‘zoom in’ their attentional resources
during proofreading...”
In sum, the study by Kaakinen and Hyönä
suggests that eye movements during reading are
sensitive to the task being carried out and that the
task has an effect on early and late measures and
more generally on the attentional span.
6
Creation of stimuli
The stimuli for the present study were created on
the basis The old man and the sea (Hemingway
1952) and its German translation. The two texts
were aligned using the Berkeley Aligner (Klein
& Petrov 2007). One-to-many items were
identified in the output of the aligner on the basis
of the following criteria: Null alignments, oneto-many alignments, sentence length.
The English ST item of each sentence was
used to construct carefully controlled sentences
of two kinds: one with the English ST item and
one with a comparable control word which could
be aligned with just one word (Figures 2 and 3).
Many of the fishermen will
worry …
Viele von den Fischern werden sich
Sorgen machen …
Figure 2: One-to-many alignment
Many of the fishermen will
laugh …
Viele von den Fischern werden lachen …
Figure 3: One-to-one alignment
One-to-one items were created for which the
equivalent a) was acceptable in the context of the
sentence and b) matched the one-to-many item in
terms of log frequency using the HAL frequency
(Balota et al. 2007) and word length in
characters. E.g. the one-to-many item [worry]
has a HAL log frequency of 10.21 and is five
letters long and the one-to-one item [laugh] has a
HAL log frequency of 9.51 and is also 5 letters
long. The mean word length for all one-to-one
and one-to-many items is 5.60 and 5.65
respectively and the difference is not significant
(t (39) = 0.76; p> .90). The mean log frequency
of all one-to-many and one-to-one items is 10.36
and 10.20 respectively and the difference is not
significant (t (39) = 0.44; p> .75). All sentences
were given to 28 translation students who did not
participate in the experiment proper. These
students were asked to rate the sentences for
plausibility and naturalness on a 1-7 likert scale.
Items which did not have a comparable
predictability (>.13) were excluded. The mean
naturalness rating for one-to-one items was 4.91
and the mean naturalness rating for one-to-many
items was 5.09 and the difference between the
ratings was not significant (t (39) = 1.49, p >
.15). The mean plausibility rating for one-to-one
items was 4.98 and the mean plausibility rating
for one-to-many items was 5.13 and the
difference between the ratings was not
significant (t (39) = .88, p > .38).
The 28 students were also asked to translate
all stimulus sentences. The translations of
sentences with one-to-many and one-to-one
items were scored in terms of the number of
German words for the English items. Two scores
were calculated: the average number of words
and the percentage of participants who used
either more than one word to translate the one-tomany item or the percentage of participants who
used only one word to translate the one-to-one
item. If the average number of target words for
one-to-many sentences was below 2, the
sentence was excluded. If the percentage of
participants who translated as predicted was
below 50%, the sentence was also excluded. Of
the 69 initially constructed sentences 29 had to
be excluded.
7
Participants
18 translators who had at least five years’
experience of regular translation into German
from English were selected from registers
available at professional bodies for translators
(IOL, ITI and AIIC). Their average professional
experience as translators was 17.75 years (SD =
11.12). All participants had English as their L2
and all participants were late bilinguals: the
mean age at which participants started learning
English was 10.50 years of age (SD = 1.56). The
average proficiency in English for all participants
was 9.00 (SD = 1.04) and the average
proficiency in German for all participants was
9.86 (SD = 0.36), on a scale of 1-10 with 1 being
not very fluent and 10 being highly fluent and as
self-assessed by participants. Data from one
participant had to be excluded due to calibration
difficulties and one further participant had to be
excluded because she had strong astigmatism.
Participants were screened for a visual acuity of
20/25 or higher using a high contrast ETDRS
chart at the viewing distance used in the
experiment.
7.1.
Apparatus
The equipment used in this experiment was an
Eyelink 2K tower-mounted eye-tracker which
requires a head restraint in order to allow for
maximum accuracy of eye movement recordings.
The keyboard was raised so that participants
could see it without having to move their heads.
The screen resolution was 1024 x 768, and the
distance of the screen from the eye-tracker was
80cm. The eye-tracker has a spatial resolution
of .01° and the position of each participant’s
right eye was sampled at 1000 Hz using corneal
reflection and pupil tracking. Sentences were
displayed on a 19 inch monitor and 1° of visual
angle subtended approximately 3.5 characters.
Black text was displayed in courier new font on a
white background.
7.2.
Materials
The stimuli consisted of 40 sentences. The
same sentence frames were used to present oneto-many and one-to-one items. As described
above, one-to-many items consisted of English
words which could be translated using more than
one German word and one-to-one items
consisted of English words which could be
translated using just one German word. One-toone and one-to-many items were comparable in
terms of word length in characters, frequency
and predictability. Sentences were 10-15 words
long and critical items were situated in the
middle region of the sentences.
7.3.
Procedure
Stimuli were presented one sentence at a time.
Calibration was carried out using a 3 point
calibration scheme at the start of the experiment,
and calibration was checked after every trial. If
calibration was not acceptable, the equipment
was recalibrated. Each participant received two
blocks of 20 sentences. In one block, participants
were asked to read the sentences for
comprehension. Participants were told that after
each presentation of a comprehension sentence,
they would be presented with a verification
sentence. Participants had to indicate whether the
verification was true or false by pressing a
response key. Comprehension was high as
judged by the correct responses to verification
sentences (M = .94, SD = .24). In the other block,
participants were instructed to translate the
sentence in their minds and they were told that
only once they had a translation in their minds
should they hit a response key, after which no
more eye movements were recorded and target
text production could start. There were no time
constraints on target text production and
participants were asked to indicate that target
text production was complete by hitting a
response key on the keyboard. After hitting this
response key, calibration was checked again and
the next sentence would appear. In the translation
block, participants were not asked to respond to
verification sentences, because translation itself
was considered to be a process which guarantees
a level of processing comparable to the reading
for comprehension condition.
At the start of each trial, participants were
presented with a red dot in the middle of the
screen and were asked to blink in order to reduce
the number of blinks during each trial. The
stimuli were presented once participants fixated a
cross at the left centre. Upon fixating the cross
the sentence was automatically presented, with
the first letter of the sentence replacing the cross.
On average, participants translated English
words as predicted on 75% of trials (during pretest, only 58% translated as predicted), i.e.
participants translated one-to-many alignments
using more than one German word and they
translated English one-to-one alignments using
just one German word. The translations were
checked for correctness and none of the
translations was deemed to be incorrect. This
was judged in relation to the instruction to not
omit anything in the translation and to produce a
translation which was literal, but correct and
acceptable. Participants were asked to imagine
that the client had asked for a rough draft of what
the text says. In this sense, no superior quality
was expected, but the translations had to conform
to German language norms and they had to be
complete.
7.4.
Data analyses
Fixations shorter than 80ms or larger than
1200ms were removed from the analysis. For the
analyses of the global and local measures, R (R
Development Core Team 2011) and the lme4
(Bates et al. 2011) and languageR (Baayen 2011)
packages were used to perform linear mixedeffects models (LMMs). For binomial variables
(such as regressions in), generalised LMMs were
conducted with the laplace approximation. Data
that were more than 2.5 standard deviations
below or above the participant’s mean for the
individual measure were excluded from analyses.
As fixed effects, the main effects of task type
(reading vs translation), target type (one-to-one
vs one-to-many), and their interaction, were
entered into the model. For the global measures
the main effects of task type were also entered
separately. As random effects, both subjects and
items were used. If visual inspection of residual
plots revealed any obvious deviations from
homoscedasticity or normality, the data were log
transformed. However as the log transformed
results showed a similar pattern to the nontransformed results, only the non-transformed
results are reported for added transparency. The
p-values were obtained using Markov Chain
Monte Carlo (MCMC) sampling and reflect the
variance of both participants and items. MCMCestimated p-values are considered significant if
they are below .05.
8
Overall task effects
Table 1 shows the means for overall task effects
per sentence. The corresponding LMMs are
listed in table 2. Global measures showed large
significant task effects. Total reading time during
translation nearly doubled in comparison to
reading for comprehension. The number of
fixations also nearly doubled during reading for
translation and participants made more than
twice as many regressions during reading for
translation as compared to comprehension. The
amplitude for progressive saccades was also
longer by nearly one character during translation.
These results suggest that the reading purpose
has a large effect on integration processes. In
addition, there was a 20ms effect on average
fixation durations suggesting that the reading
purpose also has an effect on lexical access.
Global Means per Task
Total Reading Time (ms)
Average Fixation Duration (ms)
Fixation Count
Regression Count
Progressive Saccade Amplitude (chars)
Reading
6,486.31
275
21.4
6.25
8.62
(207)
(2.66)
(0.62)
(0.24)
(0.14)
Translation
13,346.58
296
37.9
14.23
9.43
(385)
(2.99)
(0.98)
(0.44)
(0.16)
Table 1: Global means per task per sentence. Means and standard errors (in parentheses) reflect all data points.
Global Measures
Intercept
Total Reading Time (ms)
β
t (SE)
Average Fixation Duration (ms)
Β
t (SE)
Fixation Count
Β
t (SE)
Regression Count
Β
t (SE)
Progressive Saccade Amplitude (chars)
Β
t (SE)
Reading vs. Translation
9,915.80
15.08 (657.50)
6,861.50
377.40 (18.18)
***
285.59
30.04 (9.5)
20.37
2.7 (7.50)
***
29.66
1.95 (15.23)
16.47
0.96 (17.09)
***
10.23
12.97 (0.79)
7.97
18.63 (0.43)
***
9.02
15.98 (0.56)
0.78
6.47 (0.12)
***
Table 2: LMMs for the overall task effects per sentence.
The significance rates reflect participant and item variability. † = p < .1* = p < .05, ** = p < .01, *** = p < .00
9
Local measures
Table 3 shows the local means for target words
for each of the four conditions (reading/one-toone, reading/one-to-many, translation/one-to-one,
translation/one-to-many), while table 4 lists the
local means for target words per task. The
corresponding LMMs are listed in table 5. Local
measures for target words mirrored the large task
effects per sentence, in that total reading time, go
past time, fixation count and regression in
probability showed large significant task effects.
Only first fixation durations showed a marginal
interaction between task and target. Additional
contrasts which investigated this interaction
revealed that the effect of target type was only
significant during translation (reading for
comprehension: β = -7ms, SE= 10.64, t = -0.63,
p > .53; reading for translation: β = 23ms, SE=
10.98, t=2.08, p < .04). Further contrasts showed
that for targets which had a predicted one-to-one
alignment, first fixation durations during
translation were not significantly different from
first fixation durations during reading for
comprehension (β = -8ms, SE = 10.78, t = -0.70,
p > .48) and for targets which had a predicted
one-to-many alignment, first fixation durations
during translation were significantly longer as
compared to reading for comprehension (β =
22ms, SE = 10.84, t = 2.03, p < .04). The most
parsimonious interpretation of these results is
that when the reading purpose is translation, both
linguistic systems are co-activated. The large
task effects suggest that co-activation is
especially important for post-lexical processes.
The interaction effect for first fixation durations
suggests that when SL and TL items share a
representation, as in the case of one-to-one
alignments, reading for translation is not
different from reading for comprehension in
terms of lexical access. If, on the other hand, SL
and TL items do not share a representation, as is
the case for one-to-many alignments, the lack of
a shared representation inhibits lexical access.
This inhibition effect is relatively large (23ms).
Local Means per condition
First Fixation Duration (ms)
Gaze Duration (ms)
Total Reading Time (ms)
Go Past Time (ms)
Fixation Count
Regressions in Probability (%)
Skipping Probability (%)
Refixation Probability (%)
Reading One-to-one
270
(8.7)
331
(13.2)
699
(41)
431
(22.5)
2.40
(.12)
45.22
(3.98)
8.55
(2.28)
25.36
(3.72)
Reading One-to-many
263
(6.8)
325
(13.9)
663
(40)
405
(28)
2.33
(0.13)
41.67
(3.96)
9.15
(2.28)
18.98
(3.36)
Translation One-toone
261
(7.6)
341
(16)
1,419
(110)
554
(46.4)
4.68
(0.30)
60.38
(3.89)
10.60
(2.51)
29.63
(3.94)
Translation One-tomany
284
(9.5)
345
(15.8)
1,337
(85)
526
(47.9)
4.52
(0.27)
63.52
(3.83)
15.48
(2.92)
20.93
(3.60)
Table 3: Local means per condition. Means and standard errors (in parentheses) reflect all data points.
Local Means per Task
First Fixation Duration (ms)
Gaze Duration (ms)
Total Reading Time (ms)
Go Past Time (ms)
Fixation Count
Regressions in Probability (%)
Skipping Probability (%)
Refixation Probability (%)
Reading
267
(5.65)
324
(9.21)
681
(28.81)
420
(18.20)
2.36
(0.09)
43.38
(2.86)
8.94
(1.64)
22.18
(2.51)
Translation
272
(6.12)
340
(10.68)
1385
(69.94)
547
(33.70)
4.61
(0.20)
62.38
(2.79)
13.20
(1.95)
25.48
(2.69)
Table 4: Local means per task. Means and standard errors (in parentheses) reflect all data points.
9
Discussion
The current study suggests that the reading
purpose has an effect on the reading process. In
line with previous research, this effect is best
explained in terms of co-activation (SL and TL).
The global task effects suggest that reading for
translation is more intense in terms of (vertical)
integration processes, than reading for
comprehension, as indexed by the large effects
on late measures. In addition, the 21ms effect of
the translation task on mean fixation durations
suggests that the co-activation occurs early and
online during lexical access. The task effect on
mean fixation durations can be seen as evidence
of early vertical processes. However, where
source and target items share a representation, as
in one-to-one alignments, co-activation does not
inhibit lexical access. The model proposed by
Schaeffer and Carl (2013) suggests that
horizontal processes occur early and vertical
processes occur late, but the present study
suggests that target language encoding process
already start very early during source text
reading. It is therefore possible to argue that the
equivalents of source text words, activated early
during lexical access, are already being encoded
in the target language during lexical access and
Local Measures
Intercept
First Fixation Duration (ms)
β
t (SE)
Gaze Duration (ms)
Β
t (SE)
Total Reading Time (ms)
Β
t (SE)
Go Past Time (ms)
Β
t (SE)
Fixation Count
Β
t (SE)
Regressions in Probability
β
t (SE)
Skipping Probability
β
t (SE)
Refixation Probability
Β
t (SE)
Reading vs. Translation
One-to-many vs. One-to-one
Task Target Interaction
269.00
29.49 (9.12)
7.21
0.94 (7.65)
-8.08
-1.06 (7.65)
-29.50
-1.93 (15.29)
330.73
18.65 (17.73)
15.08
1.16 (13.03)
2.50
0.19 (13.05)
-7.93
-0.30 (26.07)
1,027.77
12.85 (79.99)
697.81
9.82 (71.07)
***
59.57
0.84 (71.08)
53.45
0.38 (142.15)
484.59
13.54 (35.78)
128.95
3.68 (35.01)
***
25.92
0.74 (35.02)
11.38
0.16 (70.01)
3.49
13.82 (0.25)
2.20
10.88 (0.20)
***
0.07
0.32 (0.20)
0.01
0.03 (0.40)
0.13
0.68 (0.19)
0.84
4.94 (0.17)
***
0.01
0.08 (0.17)
-0.32
-0.95 (0.34)
-2.17
-8.78 (0.25)
0.46
1.91 (0.24)
-0.11
-0.47 (0.24)
-0.21
-0.43 (0.49)
-1.45
-6.16 (0.24)
0.14
0.65 (0.22)
0.46
2.14 (0.22)
0.09
0.22 (0.43)
†
Table 5: LMMs for local effects on target words.
The significance rates reflect participant and item variability. † = p < .1* = p < .05, ** = p < .01, *** = p < .00
are being integrated and reordered later into
larger representations during scanning and rescanning.
The interaction effect for first fixation duration
is relatively isolated and it is surprising that later
measures do not show any interaction. For the
purposes of this experiment, reading for
translation has been procedurally separated from
target text production. However, translators tend
to read and write in close succession (Dragsted
2010) or at the same time. It is likely that, if ST
reading and TT production are not procedurally
separated, there will also be an interaction in
later measures. The stimuli used in Macizo and
Bajo (2006) employed interlingual homographs
and cognates. Due to their similarity in the SL
and the TL, it is not surprising that they lead to
co-activation. The items in the current study
were neither homographs nor cognates. Despite
this, it could be shown that the reading purpose
activates the TL. The use of an ecologically more
valid experimental design than in the more
controlled studies and an eye-tracker with a
higher temporal and spatial resolution than those
used in most studies investigating translation has
made it possible to chart the time course of an
effect of the reading purpose on eye movements.
Reading for translation can enrich research on
eye movements during normal reading and the
pre-test carried out for this study makes it
possible to predict TT production. This is
important, and, to the authors’ knowledge, this is
the first study which investigates translation of
longer strings of text in this way.
TTs have been predicted in a similar way for
isolated words ( e.g. Keatley et al. 1994;
Duñabeitia et al. 2010) and these priming studies
suggest that translation equivalents share a
representation. The current study supports this
and further suggests that when equivalents do not
consist of the same number of words, they are
less likely to share a representation. While this
finding is encouraging, it is important to stress
that replication across languages is necessary.
However, as detailed above, it is likely that the
literal translation hypothesis is not restricted to
only a subset of language combinations. The
current study supports Tirkkonen-Condit’s (2004:
183) view that the “tendency of the translating
process to proceed literally to a certain extent” is
indeed universal and it also supports the unique
items hypothesis. This hypothesis can be seen as
a corollary of the literal translation hypothesis, if
literality is defined in terms of similarity between
ST and TT. The one-to-many items used in this
study are unique items in the sense defined by
Tirkkonen-Condit (Tirkkonen-Condit 2004: 177)
TT items of the one-to-many items used in this
study always had an English back translation
which was similar to the German TT. The
predicted translation for e.g. the English one-tomany item [knowing] was [da er wusste], the
back translation of which is [because he knew],
which is more similar to the German expression
[da er wusste] than to the English ST item.
ST
… knowing …
TT
… da
er wusste …
Back Translation … because he knew …
Figure 4: Unique ST item [knowing]
Tirkkonen-Condit (2004: 183) argues that unique
items are less frequent in translated text because
“…there is nothing in the source text that would
trigger them off as immediate equivalents.” In
Tirkkonen-Condit’s study, the unique items’
occurrence in target texts is investigated. The
results are interpreted in terms of the effect of
non-unique ST items on TTs. The current study
investigated the effect of unique items in the
opposite direction, i.e. the effect of unique ST
items on pre-translation. The unique items
hypothesis postulates that unique items are less
frequent in TTs, because the similarity of a nonunique TT item to its ST item facilitates its use
and the non-similarity of a unique TT item to its
ST item inhibits its use. The inhibition which
explains the lack of unique items in TTs also
operates in the opposite direction, as evidenced
by the inhibition effect on first fixation durations
for one-to-many items. Malmkjær argues that
“…Tirkkonen-Condit’s study suggests that what
determines the outcomes of [Toury’s law of
interference] is the target pole, if not alone, then
as much as or more than the source pole…”
(2005: 18) Malmkjær further argues that unique
items are “…underrepresented in a translator’s
mental lexicon while he or she is translating…”
(2005: 18) Co-activation of the two linguistic
systems during translation facilitates the
activation of TT items which are similar to ST
items and inhibits TT items which are not similar
to ST items or unique to the TL: the reading
purpose (translation) gives preference to items
which share a maximum of aspects in both
languages, which is why items which are unique
to the TL have a smaller likelihood of being
selected. The current study suggests that what is
true of the TL in terms of co-activation is also
true of the SL: an item which is unique to the SL
inhibits ST processing when the reading purpose
is translation, i.e. when both linguistic systems
are co-activated. Co-activation of the two
linguistic systems facilitates both ST and TT
items which are similar to each other and inhibits
items which are not. In this sense, the current
study study also lends support to Toury’s “law of
interference” (1995: 275), in that items which
share a representation in both languages facilitate
pre-translation. In this sense, co-activation can be
seen as a heightened activation of shared
representations: translation heightens the
activation of shared representations or lowers
their activation threshold (Paradis 2004).
The stimuli were selected on the basis of the
output from the Berkeley Aligner (Klein &
Petrov 2007). The literal translation hypothesis
was tested by aligning all 69 sentences initially
created with the Berkeley aligner and by
comparing the null alignments in the one-tomany sentences with the null alignments in the
one-to-one sentences. Both kinds of ST
sentences are exactly the same apart from one
word, so differences in the alignment can be
attributed to differences in the critical words.
Null alignments can be seen as a failure of the
aligner, since the stimulus sentences, as aligned
by a human, have very few null alignments. The
average number of null alignment for the one-toone sentences is 0.53 (SD = .76), whereas the
average number of null alignments for the oneto-many sentences is 2.06 (SD = .53). The
difference between these means is highly
significant (t(68)= 9.55, p< .001). The Berkeley
aligner produces significantly more null
alignments for ST and TT sentences with
differing lengths and with one-to-many
alignments than for sentences pairs with the
same length and without one-to-many alignments.
Figure 18: Null alignments for one-to-many and oneto-one items.
This suggests that the aligner is inhibited by
one-to-many alignments in a similar way to the
human translator. The human translator can
overcome this inhibition and instead of omitting
these items, s/he easily finds equivalents. In this
sense, the Berkeley Aligner can be seen as a
computational implementation of the literal
translation hypothesis.
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