Relating Production Units and Alignment Units

in Translation Activity Data

Michael Carl and Arnt Lykke Jakobsen

Dept. of International Languages Studies & Computational Linguistics

Copenhagen Business School, 2000-Frederiksberg, Denmark

Abstract. The deﬁnition and characterisation of Translation Units (TUs) in hu-

man translation is controversial and has been described in many different ways.

This paper looks at TUs from a translation process perspective: we investigate

the sequences of keystrokes which have been typed during translation production

and re-deﬁne TUs in terms of text production units (PUs). We correlate those

units with translation equivalences in the translation product, so-called alignment

units (AUs) and compare the translation performance of student and professional

translators on a small translation task of 160 words from English into Danish. In

contrast to what has frequently been assumed, our data reveals that TUs are rather

coarse, as compared to the notion of ‘translation atoms’, comprising several AUs,

and they are particularly coarse for professional translators.

1 Introduction

There is a large body of literature on segmentation in translation which can be separated

into two fundamentally different kinds: research into human translation processes seeks

to ﬁnd basic segments of activities in the translation process, whereas others think of

the segments more statically as properties observable in the translation product i.e.

correspondences in pairs of texts as a result of a translation process. Accordingly, there

is a confusion in the usage of the term “translation unit” (TU), which sometimes refers

to the former and sometimes to the latter type of unit. However, it is by no means

clear that there is isomorphism between the units that a translator has in mind during

translation and the correspondencies which can be made out later in the ﬁnal translation

product.

According to [1] translation units are lexicological units: they are signs, each with

de Saussures two components, the signiﬁant and the signiﬁ

e. Such a unit is “the small-

est segment of the utterance where the cohesion of signs is such that they cannot be

translated separately” [1, p:16]. We will refer to this kind of unit, which can be detected

as translation equivalences in the ﬁnal translation product, as Alignment Units (AUs).

In line with [2, p:14] we adopt the more dynamic view, seeing a TU as “the section

of text which the translator focusses on at any one time”. Similarly, for [3, p:254] the

TU is the “translator’s focus of attention at a given time in the translation process”. This

deﬁnition implies that TUs cannot be directly observed in the user activity data (UAD),

such as in the translator’s keystrokes or gaze movements, but relate to activations in

Carl M. and Lykke Jakobsen A.

Relating Production Units and Alignment Units in Translation Activity Data.

DOI: 10.5220/0003024400370046

In Proceedings of the 7th International Workshop on Natural Language Processing and Cognitive Science (ICEIS 2010), page

ISBN: 978-989-8425-13-3

the translator’s mind. However, we assume that there is no appreciable lag between a

translator’s focus of attention and what he is typing.

It therefore becomes possible to investigate units of translation production and as-

sume that they are related to TUs. We deﬁne a production unit (PU) as a sequence of:

1. successive keystrokes in time that are not interrupted by a pause longer that a given

PU segmentation threshold. Only deletion and insertion keystrokes are considered.

Navigation activities, using the mouse or combinations of keystrokes are ignored.

2. successive keystrokes in text that produce a coherent piece of text. Text producing

and deleting activities are part of the same PU only if they are in close proximity to

each other.

The boundary of a PU is thus deﬁned to lie between two successive keystrokes that are

separated by more than a certain delay in time, or if the second keystroke contributes

to production or revision of a different piece of text [5]. Thus, deletions and corrections

are possible in one PU if they are within the vicinity of the current cursor position.

Note that not all conceivable PUs are equally likely to reﬂect a unit of the trans-

lator’s focus of attention. As is the case for AUs, TUs must also comprise a coherent

set of signs that “cannot be translated separately”, if, as [6] suggests, skilled translators

proceed “little at a time” combining source text (ST) and target text (TT) segments, so

that “each segment forms a fragment of bi-text in their minds”.

The paper investigates various PU segmentation thresholds to maximise the like-

lihood of the PU being part of a TU. We expect PUs to satisfy two properties: 1.

PUs should represent complete meaning entities rather than arbitrary sequences of

keystrokes. 2. PUs should align with AU boundaries, rather than crossing their lines.

We are looking for a PU segmentation threshold that maximises these properties.

In section 2 we describe the experimental setup for data acquisition, the hard and

software used to collect the UAD as well as details about the translation task. Section 3

looks into details and analyses a data segment in depth. Section 4 applies the analysis

technique to a set of 24 translations and provides a large number of PU correlations.

2 Data Acquisition

We base our research on a translation experiment in which 12 professional and 12 stu-

dent translators produced translations using the version of the Translog [7] software

developed as part of the EU Eye-to-IT project

. Translog presents the ST in the upper

part of the monitor, and the TT is typed in a window in the lower part of the monitor.

Note that this is an adapted version of the “eye-mind assumption” [4], which hypothesises that

“there is no appreciable lag between what is being ﬁxated and what is being processed” [4,

p:331].

Some people read electronic texts by moving the cursor on the ﬁxated words. If navigation

keystrokes were to count in PUs, we would obtain long PUs consisting only of navigation

activity.

The keylog portion of the software can be obtained from www.translog.dk

cogs.nbu.bg/eye-to-it/

When the start button is pressed, the ST is displayed and eye movement and keystroke

data begins to be registered. The task of the translator is to type the translation in the

lower window. After having completed the translation, the subject presses a stop button,

and the translation, together with the translation process data, are stored in a log ﬁle.

This so-called User Activity Data (UAD) is then transformed into a relational data

structure which allows us to map eye movements and keyboard actions onto ST and

TT positions and vice versa: [8,9] describe how keyboard actions are related to the TT

words to which they contribute and how TT words are mapped on ST words, so that

for (almost) each keystroke, we can determine to which ST translation it contributes. In

order to do so automatically, the relational data structure requires, besides the prepara-

tion of the keyboard and eye movement data, also the alignment information between

the ST and the TT.

For the data set of the 24 translations we semi-automatically aligned the ST and TT

and converted the alignment and UAD into the required structure.

Killer nurse

| {z }

receives

| {z }

four

| {z }

life sentences

| {z }

z }| {

Mordersygeplejer

z }| {

modtager

z}|{

ﬁre

z }| {

domme p˚a livstid

Fig.1. A segment of the product data shows four AUs aligning 6 ST words and 6 TT words; AUs

were manually aligned.

3 Analysis of Production Units

In this section we look more closely at a time segment of 20 seconds in which the

translation shown in ﬁgure 1 was typed. We develop and discuss properties of the PUs

and show how these properties generalise to the entire translation session. In section 4

we apply these criteria to all 24 translations.

Details of the keystroke segmentations are reproduced in tables 1 and 2. During

these 20 seconds, 56 keystrokes were produced. Each different PU segmentation thresh-

old groups the data differently with different properties of the produced PUs.

The 400ms segmentation in table 1 groups the 56 keystrokes into 10 PUs. Due to the

pauses of 894ms and 488ms after “y” and “j” respectively, the word “Mordersygeple-

jerske” is segmented into 3 PUs. Note that the sufﬁx “ske” is later deleted and thus does

not appear in the ﬁnal translation in ﬁgure 1. On the other hand, the two AUs “mod-

tager” and “ﬁre” are written smoothly so that these two AUs are grouped into one PU.

The “start” column in table 1 gives the timestamp when the PU was started to be typed.

The “dur” column shows the time needed to complete the PU, and “pause” shows the

interval of time following the PU until the next keystroke was processed. Note that the

duration is 0 if the PU contains only one keystroke. The “AU” column shows which

AUs are generated by each PU. Thus, the ﬁrst three PUs all contribute to the translation

of the ﬁrst AU

while the fourth PU contains AU

and AU

A number of corrections occur in the following segments: ﬁrst the letter “s” is writ-

ten and then deleted in the next segment. The deletion is indicated in brackets “(s)”.

Table 1. Properties of PUs as generated with 400ms segmentation.

NR start dur pause type AU PU

1 10485 1397 894 SAWU 1 Mordersy

2 12776 944 488 WUWU 1 geplej

3 14208 717 432 WUSA 1 erske

4 15357 2177 2719 SASA 2,3 modtager ﬁre

5 20253 0 702 SAWA 4 s

6 20955 0 4141 WUWU 4 (s)

7 25096 118 886 WUWU 4 li

8 26100 192 669 WUWU 4 (li)

9 26961 1051 536 SASU 4 domme p˚a

10 28548 907 749 SUSA 4 livstid

Then “li” is produced and then deleted “(li)”. Finally the translation “domme p˚a livstid”

is typed but segmented into 2 PU due to the delay of 536ms after “p˚a”.

The degree to which a PU coincides with a word in the TT translation or an AU

boundary in the ST is indicated by its type. A PU can start and/or end at a word bound-

ary. For instance “livstid” is a complete word and PU

starts and ends at a word sepa-

rator. “Mordersy” in contrast is the beginning of a word while “erske” is the ending of

that word. Accordingly, PU

starts at a word boundary and PU

ends with it.

In addition, a word (or segment) in the target language can start and/or end at an

AU boundary. For instance, “livstid” is the last part of a compound which is grouped

in AU

, and so it ends but does not start at the boundary of AU

. “domme p˚a” is

the beginning of that same compound and so PU

starts, but does not end, at the AU

boundary.

Thus the type of a PU consists of four positions (bits), each of which can take two

values: The ﬁrst two positions indicate properties for the beginning of the PU, and the

last two positions indicate properties of its end. The values indicate whether or not the

PU aligns with word boundaries in the target language, and whether or not it aligns with

boundaries of the AUs of which it is a translation. These values are represented by the

letters [SWAU] which have the following meanings:

S ﬁrst/last character of PU was a word separator (space, comma, semicolon, colon)

or immediately following a separator.

W ﬁrst/last character of PU was not a word separator (and not immediately following

a separator)

A ﬁrst/last character of PU was at an AU boundary.

U ﬁrst/last character of PU was not at an AU boundary.

Thus, “SAWU”, as in the ﬁrst line of table 1 indicates that the PU “Mordersy” starts

at the beginning of a word (S), and it aligns the beginning of an AU (A). The last two

letters indicate that this PU ends in the middle of a word (W) and in the middle of an

AU (U). Ideally, as discussed in the introduction, a PU should start and end with a word

separator and/or an AU boundary, such as “modtager ﬁre” in line 4 of table 1. Segmen-

tations in the middle of a word would (perhaps) indicate that attention is focussed on

spelling or typing problems, rather than on translation. Thus, a PU of type “WUWU”

(e.g. line 2: “geplej”) indicates that the segment neither starts nor ends at a word or

an AU boundary. Such segments provide little insight into the cognitive processes of

translators, since they do not coincide with meaning units, as e.g. words and AUs do.

However, in the introduction we have argued that PUs should represent signs, with a

signiﬁ

e, which is difﬁcult to see in the case of “geplej” or “li”.

Four out of the 10 segments in table 1 are of this type, indicating that a 400ms

segmentation threshold does not correspond to the “cognitive” rhythm of segmentation

that we are looking for.

Table 2. Segmentation 800ms (above) and 1500ms (below) of the same 56 keystrokes from ta-

ble 1. The notation “s(s)” and “[s]” are equivalent, meaning typing and deletion of the bracketed

expression.

start dur pause type AU PU

10485 1397 894 SAWU 1 Mordersy

12776 4758 2719 WUSA 1,2,3 geplejerske modtager ﬁre

20253 702 4141 SAWU 4 s(s)

25096 118 886 WUWU 4 li

26100 4104 3064 WUSA 4 (li)domme p˚a livstid

10485 7049 2719 SASA 1,2,3 Mordersygeplejerske modtager ﬁre

20253 702 4141 SAWU 4 [s]

25096 5108 3064 WUSA 4 [li]domme p˚a livstid

The 800ms PU pattern in table 2 generates only half the number of PUs of those

for the 400ms segmentation. Only one (20%) of them is a “WUWU” segment while the

remaining four (80%) either start or end with a word separator. This pattern is even more

obvious in the 1500ms segmentation of table 2, where all segments show a linguistically

plausible beginning or end. On the other hand, segmentation with longer thresholds

includes more characters and subsumes more than one AU. Thus, the average lengths

of 400ms, 800ms and 1500ms segments of the ﬁrst 56 keystrokes are 5.6, 11.2 and 18.7

keystrokes, respectively.

Table 3 provides those ﬁgures for the entire translation by P13 with various segmen-

tation thresholds for PU patterns. It shows the number of PU segments, their average

length in characters (#char), the percentage of linguistically plausible SASA and S.S.

segments and the percentage of the implausible WUWU segments. Under the 400ms,

800ms and 1500ms segmentation, translator P13 produced 98, 39 and 23 PUs. The

optimum PU segmentation threshold seems to be around 800ms to 1000ms, where a

maximum number of segments are linguistically plausible, and at the same time the

segments do not comprise too many AUs.

4 Segmentation in Writing

This section investigates various PU segmentation thresholds for all 24 translations.

As mentioned earlier in section 2, an English text of 160 words was translated by 12

professional and 12 student translators into Danish. As shown in table 4, there is a

large variance in both overall time needed for the translation and in the number of PUs

produced. Professionals took on average slightly more than 5 minutes to translate the

Table 3. Number and properties of PUs for translator P13, generated under various segmentation

thresholds.

thresh. #PU #char %WUWU %SASA %S.S.

200ms 355 2.6 27.04 33.80 37.75

400ms 98 9.4 16.33 37.76 44.90

800ms 39 23.5 2.56 43.59 64.10

1000ms 30 30.5 0.00 50.00 73.33

1500ms 23 39.8 0.00 47.83 69.57

2500ms 14 65.4 0.00 42.86 71.43

text (320 seconds), whereas students took more than 6 minutes (379 seconds). That

is, on average professional translators needed 84% of the time needed by students to

produce the translation. For both groups there was approximately a factor of 3 between

the fastest and slowest translator.

Table 4. Translation time (T-time) and number of PUs for different kinds of segmentation for the

12 professional and 12 student translators.

Professionals #PU

Tr. T-time 400ms 800ms 1500ms

P15 170636 70 18 3

P14 209142 105 38 13

P2 258782 96 38 15

P13 265762 98 39 23

P20 281148 162 50 22

P3 316730 138 62 32

P1 349750 107 33 18

P21 352497 154 69 33

P7 362404 141 78 50

P8 379272 114 58 33

P9 389931 145 71 47

P19 510366 177 84 46

av. 320535 126 53 28

Students #PU

Tr. T-time 400ms 800ms 1500ms

S6 228584 107 39 15

S18 260454 133 50 17

S16 285898 125 57 31

S24 322110 158 81 38

S11 350663 131 70 43

S4 353340 174 63 31

S12 374499 127 76 50

S10 377134 111 71 49

S17 411156 132 68 35

S23 425860 176 99 48

S22 507021 170 105 56

S5 654681 218 116 70

av. 379283 147 75 40

Similarly, on average students produced the translations with 30% more PUs than

professionals, and there was a factor of almost 4 between the smallest and largest num-

ber of PUs produced for both groups. These relations are also plotted in ﬁgure 2. The

black rectangular symbols in ﬁgure 2 indicate the relation between the translation time

and the number of segments produced with the 400ms PU segmentation threshold, the

triangular symbols those of the 1500ms PU threshold, and yellow squares and diamonds

represent student and professional translators, respectively. All translation thresholds in-

dicate a strong correlation between translation time and the number of segments. That

is, the more the translation is fragmented into a larger number of segments, the longer

is the translation time.

This correlation is not necessary: many fast-typed segments with short pauses in between them

could amount to the same overall translation time as few segments with long inter-segmental

pauses.

Fig.2. The graph shows a strong correlation between the translation time (horizontal) and the

number of PUs (vertical) for three types of segmentation.

The PUs produced by professionals are, on average, longer and the time needed per

PU is (on average) higher for professionals than for those of the students.

Table 5. Average duration and length of PUs with different segmentation thresholds, and the

average typing speed (keystrokes per second) for professional and student translators. (Typing

averages are high because only within-PU keystroke intervals were counted and because PUs

with only one keystroke are recorded with no time duration).

Professionals Students

400ms 800ms 1500ms 400ms 800ms 1500ms

average PU duration in ms 1001 3113 6896 854 2216 5024

average PU length in chars 7.46 17.61 33.55 6.37 12.54 23.24

average (PU chars/PU dur.) 8.22 5.44 4.83 7.18 5.70 5.02

median (PU chars/PU dur.) 7.15 5.61 4.77 6.97 5.53 4.80

Average duration and length (in characters) for various PU segmentation thresholds

is given in table 5. Depending on the PU thresholds, students generate, on average,

15% to 30% shorter segments in length as well as in duration. They also produce more

segments than professional translators, as we have already shown above in table 4 and

in ﬁgure 2.

However, the typing speed in terms of characters per time within each PU does not

seem to vary much between professionals and students. The values in table 5 indicate

that average and median inner-segment typing speed between successive keystrokes de-

creases with a growing PU threshold. For instance, at 800ms segmentation threshold,

professionals produce 5.44 characters, while students produce 5.7 keystrokes per sec-

ond. The typing behaviour of students is more fragmented than that of professionals,

with more pauses longer than the segmentation threshold, but when the segments are

actually typed, the speed with which successive keystrokes are produced seems to be

identical for both groups.

With a segmentation threshold of 1500ms the longest PU was produced by translator

P15, with 183 characters. With a threshold of 800ms the longest PU was produced by

translator P14, comprising 18 AUs and the following 153 characters

fordi Norris ikke kunne lide at arbejde med [g]ældre mennesker. Alle hans ofte

var s[v][a]vagelige ældre kvin[ger ]der med hjerteproblemer. ALle ville

This TU starts with a subordinate clause, that is, the second half of a sentence, it

then contains a whole sentence and ends with the beginning of a third sentence at word

position 150.

Table 6. Number and properties of PUs for different segmentation thresholds: SASA: PUs start

and end with a word separator and an AU boundary, S.S.: PUs start and end with a word separator,

WUWU: PUs start and end in the middle of a word.

200ms 400ms 800ms 1000ms 1500ms 2500ms

#PU 8060 3293 1557 1245 842 524

%SASA 11.39 37.16 48.96 49.72 41.92 37.40

%S.S. 24.54 45.68 53.64 54.22 55.11 50.0

%WUWU 36.72 20.77 7.32 6.27 4.75 4.96

A more detailed listing of the types of PU is given in table 6. The table shows that the

type and distribution of PUs change under different PU thresholds. It gives an overview

of the number of PUs produced with 6 different thresholds and shows the percentage

of linguistically more and less meaningful segments, similar to table 3. The table does

not make a distinction betwee students and professional translators. With increasing

segmentation time, the number of generated segments decreases, and the percentage of

meaningful segments increases. A dramatic change of this effect can be observed up to

ca. 800ms: the meaningless “WUWU” segments fall below 10% and the linguistically

coherent ones grow beyond 50%. Beyond this margin, values change less quickly.

5 Conclusions

The paper investigates activity data of student and professional translators: An English

text of 160 words was translated by 12 professional and 12 student translators into Dan-

ish.

All keystrokes and eye movements were recorded using the Translog software.

Note that the typing speed was computed as the lapse of time between two (or more) successive

keystrokes, so that a PU consisting of a single keystroke would count as 0ms duration. This

explains the relatively fast typing speed.

deletions are in square brackets. The notation “[ger]” means that ﬁrst “ger ” was produced

(including blank) and then deleted.

The source text of this passage is: “that Norris disliked working with old people. All of his

victims were old weak women with heart problems. All of them could”

The ST is reproduced in the Appendix.

We investigate and correlate three types of unit: production units (PUs) of keystrokes

where no two keystrokes are separated by a pause longer than a given threshold, align-

ment units (AUs) which reﬂect translation equivalences in the translation product, and

translation units (TUs) which represent the translators’ focus of attention.

The goal was 1) to determine a threshold for the segmentation of PUs such that

they most likely conform to criteria of TUs, and 2) to investigate properties of PUs for

students and professional translators.

Various thresholds of maximal delay between successive keystrokes are explored to

group sequences of keystrokes into PUs. PUs are considered intelligible if their bound-

aries coincide maximally with linguistic (i.e. word) boundaries in the target language

and with the boundaries of translation atoms as deﬁned by AUs. Our investigationshows

that pauses in writing activity of approximately 1000ms length produce segments of

maximal linguistic plausibility, and are, thus, indicative of cognitive processing units.

That is, a new TU is likely to have started if a pause of 1 second or more can be observed

with no keystroke.

Our ﬁndings can be summarised as follows:

– The number of PUs correlates strongly with translation time: the longer the trans-

lation time, the more fragmented is the translation into segments (ﬁgure 2).

– Professional translators produce translations more quickly than students (table 4).

– Professional translators produce longer PUs than students in terms of time, as well

as in terms of the number of characters (table 5).

– Professional and student translators type PUs at approximately the same speed (ta-

ble 5).

– Longer PUs coincide better with word boundaries than shorter PUs (table 6)

Only a small percentage of PUs coincide with a single AU. Rather than producing a

minimal unit or a “translation atom”, we ﬁnd that translators produce maximal seg-

ments, which seem to increase with the capacity and training of the translator. In a

further study, we intend to investigate properties of these segments in more depth so as

to construct an inventory of cognitive operations associated with the PUs.

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Appendix: Source Test

Killer nurse receives four life sentences

Hospital Nurse Colin Norris was imprisoned for life today for

the killing of four of his patients. 32 year old Norris from

Glasgow killed the four women in 2002 by giving them large

amounts of sleeping medicine. Yesterday, he was found guilty

of four counts of murder following a long trial. He was given

four life sentences, one for each of the killings. He will have

to serve at least 30 years. Police officer Chris Gregg said

that Norris had been acting strangely around the hospital. Only

the awareness of other hospital staff put a stop to him and to

the killings. The police have learned that the motive for the

killings was that Norris disliked working with old people. All

of his victims were old weak women with heart problems. All of

them could be considered a burden to hospital staff.