Changes of word class during the

translation process

Insights from a combined analysis of keystroke logging

and eye-tracking data

Tatiana Serbina, Sven Hintzen, Adjan Hansen-Ampah, Paula Niemietz,

Stella Neumann

Translation in Transition, Germersheim, 29.-30.01.2015

A HumTec Boost Fund Project funded by the Excellence Initiative of the German

State and Federal Governments

Overview

Translation shifts

Grammatical complexity

Aims of the study

Methodology

Product-based analysis: word class changes ST vs. TT

Process-based analyses: eye-tracking analysis, word class

changes in intermediate versions of translations

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Empirical translation studies

Product-based studies

Method: corpus analyses

Typical research questions: translation shifts or translation

properties

Process-based studies

Method: translation experiments (frequently keystroke

logging and eye-tracking)

Typical research questions: translators’ styles, levels of

expertise and their effect on the translation process

Our research

Treating keystroke logs as a corpus (cf. e.g. Alves &

Magalhães 2004, Alves & Vale 2009, 2011)

A combination of product-based and process-based

perspectives3

GTrans: Ein Blatt Papier zusammen zu knüllen, erscheint einfach

und erfordert wenig Anstrengung; [die [Verhaltensweise]Noun des

Papierknäuels]NP zu erklären, ist dagegen eine völlig andere Sache.

(KLTC PROBRAL GT7)

Translation shifts

Translation shifts: differences between source and target texts,

e.g. part of speech change or change of semantic perspective

(Čulo et al. 2008, Cyrus 2009, Halverson 2007)

Changes of word class – transpositions (Vinay and Dalbernet

1958/1995, 36)

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EO: Crumpling a sheet of paper seems simple and doesn't require

much effort, but explaining [why the crumpled ball [behaves]Verb the

way it does]Clause is another matter entirely.

Word classes – contrastive

difference

German: nominal word classes - 40.21%

verbal classes - 22.53% ratio of 1.784

English: nominal word classes - 41.39%

verbal word classes - 25.47% ratio of 1.625

More pronouns in German (8.45%) than in English (5.46%)

German appears to be more nominal than English (Hansen-

Schirra, Neumann, and Steiner 2012, 77-78)

In the translations into German: more shifts from verbs to nouns

and fewer shifts from nouns to verbs than in the opposite

translation direction (Čulo et al. 2008, 50)

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Grammatical complexity

Association with different levels of grammatical complexity

Verbs – possible indicator that the process is realized canonically

through a clause

Nominalizations – may result in a more condensed and thus

grammatically more complex version (Halliday and Matthiessen

2014, 715).

Translation: understanding of the more complex units in the ST

could involve their paraphrase with grammatically more simple

structures in the TT

(Steiner 2001, Hansen-Schirra, Neumann, and Steiner 2012, 257-261)6

EO: [After the [crumpling]Noun of a sheet of thin aluminized Mylar]PP, the

researchers placed it inside a cylinder.

GTrans: [Nachdem sie ein dünnes Blatt aluminiumbeschichtetes Mylar

[verknittert]Verb hatten]Clause, gaben sie es in einen Zylinder. (KLTC

PROBRAL GT3)

Aims of the study

Analysis of POS distribution and shifts between main word

classes

This study concentrates on nouns & verbs and investigates the

cognitive effort during the translation process depending on

the word class in the original

type of shift in the translation

Analysis of intermediate versions in the keystroke logging data

Assumptions:

due to the contrastive difference, the translation direction English-

German may be characterized through shifts from verbs to nouns

due to the process of understanding related to the grammatically

dense noun phrases in ST, translations into German may be

characterized through shifts from nouns to verbs (in the

intermediate or final versions)

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Our translation process data

Translation experiment (Neumann et al. 2010)

Translation direction: English-German

Subjects

8 professional translators

8 physicists

Material

Two versions of an authentic text with ten integrated stimuli

Abridged version of a popular-scientific text published in the

journal Scientific American Online

Apparatus

Tobii 2150 remote eyetracker, software Tobii Studio 1.5

Keystroke logging software Translog

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Keystroke Logged Translation

Corpus

The corpus consists of:

2 versions of the original (source texts)

16 translations (target texts)

16 log files (process texts)

Corpus size (comprising STs and final TTs):

approx. 3,650 words

Corpus register: Popular Scientific writings

(Serbina, Niemietz, and Neumann, forthcoming)

Automatic POS annotation of ST and TT using TreeTagger

(Schmid 1994)

Manual alignment between ST and TT words using the

alignment tool (Hansen-Ampah 2014) based on the alignment

guidelines (Samuelsson et al. 2010)9

Methodology

Manual extraction of ST words belonging to main word classes

and the aligned TT words

Translation pairs selected for further analysis:

Shifts between nominal and verbal variants

Random samples of verbs and nouns that do not contain a shift in

the final translation

Keystroke data: identification of intermediate versions for the

selected ST words

Eye-tracking data: calculation of total fixation duration as a

concrete indicator of cognitive effort for the selected ST words

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POS distribution in ST and TT

English STs German TTs

Nouns 32,75% (113/345) 27% (882/3267)

Verbs 17,39% (60/345) 15,81% (511/3267)

Adjectives 11,30% (39/345) 9,77% (319/3267)

Adverbs 4,35% (15/345) 5,17% (169/3267)

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More nouns and verbs in English originals than in German

translations

Technical problem: compound nouns counted as several

nouns in English but as one in German (Čulo et al. 2008,

49)

Types of word class shifts

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Absolute numbers % of all shifts

VERB → NOUN 37 49,95%

ADJ → NOUN 23 17,04%

NOUN → VERB 16 11,85%

VERB → ADJ 14 10,37%

ADV → PP 14 10,37%

NOUN → ADJ 10 7,41%

VERB → ADV 9 6,70%

ADV → ADJ 6 4,44%

NOUN → ADV 4 2,96%

ADJ → ADV 2 1,48%

Types of word class shifts II

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English

ST verb

English

ST noun

No shift 350 776

Shift 60 30

The translation direction

English-German is

characterized through shifts

from verbs to other word

classes, in particular to nouns

Cognitive effort I

Does the translation of nouns require more cognitive effort than

the translation of verbs?

Cognitive effort is measured using log-transformed values for

total fixation duration normalized per character

14

Means: noun -1.9

verb -1.78

t = -0.7, df = 94.85, p-value = 0.48

GTrans: Statt zu einer endgültigen festen Größe zusammenzufallen, nahm

die Höhe des zusammengeknüllten Papierballs weiter ab, und zwar auch

noch drei Wochen, [nachdem das Gewicht [angewendet]Verb wurde]Clause.

(KLTC PROBRAL GT5)

Cognitive effort II

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Means: n-v -2.05

v-n -1.65

t = -1.57, df = 33.1, p-value = 0.13

Slightly lower mean for the total

fixation duration associated with shifts

from nouns to verbs could be

potentially explained through reduction

of grammatical complexity

EO: Instead of collapsing to a final fixed size, the height of the crushed ball

continued to decrease, even three weeks [after the [application]Noun of

weight]NP.

Intermediate versions I

Verb to Noun shifts: Verb Verb Noun (3x)

Verb Noun Noun (1x)

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EO: Crumpling a sheet of paper seems simple and doesn't require much

effort, but explaining [why the crumpled ball [behaves]Verb the way it

does]Clause is another matter entirely.

GTrans: Ein Blatt Papier zusammen zu knüllen, erscheint einfach und

erfordert wenig Anstrengung; [die [Verhaltensweise]Noun des

Papierknäuels]NP zu erklären, ist dagegen eine völlig andere Sache. (KLTC

PROBRAL GT7

GTrans_i: Ein Blatt Papier zusammen zu knüllen, erscheint einfach und

erfordert wenig Anstrengung, jedoch zu erklären, [warum der

zeras Papierknäuel sich so [verhält]Verb, wie es das

tut]Clause, ist eine völlig andere Sache.

GTrans: [Nachdem sie ein dünnes Blatt aluminiumbeschichtetes Mylar

[verknittert]Verb hatten]Clause, gaben sie es in einen Zylinder. (KLTC

PROBRAL GT3)

Intermediate versions II

Noun to Verb shifts: Noun Noun Verb (1x)

Noun Verb (Verb) Verb (2x)

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EO: [After the [crumpling]Noun of a sheet of thin aluminized Mylar]PP, the

researchers placed it inside a cylinder.

GTrans: [Nachdem sie ein dünnes Blatt aluminiumbeschichtetes Mylar

[verkrumpelt]Verb hatten]Clause, gaben sie es in einen Zylinder.

GTrans: [Nachdem sie ein dünnes Blatt aluminiumbeschichtetes Mylar

[verknäuelt]Verb hatten]Clause, gaben sie es in einen Zylinder.

Conclusion & Outlook

An application of a Keystroke Logged Translation Corpus to

triangulate product and process data

Shifts from verbs in the ST to nouns in the final TT

the most pervasive type of shifts in the translation direction

English-German

Verbs shifted to nouns are fixated slightly longer than nouns

shifted to verbs

Both types of shifts can involve intermediate stages (either ST

POS or TT POS, i.e. a synonym of the item in the final TT)

Determining cognitive effort based not only on the shift in the

final but also intermediate translation versions more data

points required

Taking into account further indicators of cognitive effort (further

combining eye-tracking and keystroke logging data streams)

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e-cosmos platform

Creating a web-based platform for different scenarios of

multimodal data integration and analysis

Translation data

Integration: text, keystroke logging and eye-tracking data

Identification of word tokens in the intermediate translation

versions and their linguistic annotation

Query tool for quantitative analyses of product and process data

(cf. Carl & Jakobsen 2006)

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e-cosmos platform

Linguistics Computer Science Information Management

Thank you for your attention!

Tatiana Serbina

serbina@anglistik.rwth-aachen.de

RWTH Aachen University

Templergraben 55

52056 Aachen

www.rwth-aachen.de

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