Corpora, registers, and metaphor: What every translator should know but was afraid to
ask
Estudos em Tradução – Teorias, Práticas e Tecnologias
PUCRS, August 31, 2012
Tony Berber Sardinha
Multidimensional analysis and translation
Main points

Translation is normally thought of as depending mostly on lexis but in fact it
also involves structure (grammar), among other levels.

Grammar is affected by the choices made by translators. These choices can
have a cumulative effect on the constitution of a text and this in turn can make
a text ‘feel different’ than what the translator had in mind.

It is hard to have control over the cumulative effect of translation choices, and
so in this part of the mini-course the goal is to raise awareness as to the
importance of grammar choices in translation rather than to present ‘the
solution’ to this issue or ‘the technique’ to handle such cases.

Register shift: If the translation is such that the typical characteristics of a
register, eg. Conversation, are translated into a different set of characteristics
(typical of another register), then the resulting text will not be a natural
conversation, but something else.

Translators translate texts and texts are shaped by register. Hence it's important
that translators become aware of register characteristics. One of these is that
they vary systematically -- particular linguistic features co-occur sytematically
in particular registers, and not in others.
1
Functional interpretation of structural characteristics
Biber, D., & Conrad, S. (2009). Register, genre, and style. Cambridge
; New York: Cambridge University Press.
What impact might a translation have on a text based on the information on this table?
2
Register
‘The register perspective combines an analysis of linguistic characteristics that are
common in a text variety with analysis of the situation of use of the variety. The
underlying assumption of the register perspective is that core linguistic features like
pronouns and verbs are functional, and, as a result, particular features are commonly used
in association with the communicative purposes and situational context of texts.’
Biber, D., & Conrad, S. (2009). Register, genre, and style. Cambridge ; New York: Cambridge University
Press, p.2.
Dimensions and Multidimensional Analysis
‘Multidimensional (MD) analysis is a quantitative approach that allows the researcher
to compare many different registers, with respect to several differ- ent linguistic
parameters – the “dimensions.” Two registers can be more or less different with respect
to each dimension. By considering all linguistic dimensions, it is possible to describe
both the ways and the extent to which registers differ from one another, and ultimately,
the overall patterns of register variation in a language.
As shown in the last section, the relative distribution of common linguistic features,
considered individually, cannot reliably distinguish among registers. There are simply too
many different linguistic characteristics to consider. However, these features work
together as distinct underlying dimensions. Each of these dimensions represents a group
of features that co-occur: the features – as a group – are frequent in some registers and
rare in other registers.’
Biber, D., & Conrad, S. (2009). Register, genre, and style. Cambridge ; New York: Cambridge University
Press, p.223.
Dimensions of register variation for English (Biber 1988, 2009)
Dimensão
1
Biber 1988
Involved versus
Informational
Production
2
Narrative versus
Non-narrative
Concerns
Explicit versus
SituationDependent
Reference
Overt Expression
of Persuasion
Abstract versus
Non-abstract
Information
On-line
Informational
elaboration
3
4
5
6
Tradução
Produção marcada
por envolvimento
versus
informacional
Propósitos
narrativos versus
não narrativos
Referência
explícita versus
dependente de
referência
Persuasão
explícita
Informação
abstrata versus
não-abstrata
Elaboração em
tempo real
Biber 2009
Involved versus
Informational
Production
Narrative versus
Non-narrative
discourse
Situationdependent versus
elaborated
reference
Overt expression
of argumentation
Abstract versus
non-abstract style
Tradução
Produção marcada
por envolvimento
versus
informacional
Discurso narrativo
versus não
narrativo
Referência
dependente de
situação versus
elaborada
Argumentação
explícita
Estilo abstrato
versus não-abstrato
(Não existente)
3
Linguistic characteristics associated with each dimension
Dimensão
Dimensão 1
Polo positivo
Private verbs
THAT deletion
Contractions
Second person pronouns
Present tense verb
DO as pro-verb
Analytic negation
Demonstrative pronouns
General emphatics
First person pronoun
BE as main verb
Pronoun IT
discourse particles
Causative subordination
Indefinite pronouns
General hedges
Amplifiers
Sentence relatives
WH questions
Possibility modals
Non-phrasal coordination
WH clauses
Final prepositions
(Adverbs
( Conditional subordination
Polo negativo
Attributive adjectives
Prepositions
Type-token ratio
Word length
Nouns
(Present participal WHIZ
deletion
(Past participal WHIZ
deletion
(Agentless passives
(Place adverbials
Dimensão 2
Polo positivo
Past tense verbs
Perfect aspect verbs
Third person pronoun
Public verbs
Synthetic negation
Present participial clauses
Polo negativo
(Word length
(Past participal WHIZ
deletion
(Attributive adjectives
Peso
Verbo privado
Apagamento de THAT
Contração
Pronome de segunda pessoa
Verbo no tempo presente
Verbo DO
Negação analítica
Pronome demonstrativo
Enfatizador
Pronome de primeira pessoa
Verbo to be
Pronome IT
Partícula discursiva
Subordinação causativa
Pronome indefinido
Atenuador
Advérbio / qualificador amplificador
Pronome relativo
Pergunta WH
Verbo modal de possibilidade
Coordenação não-frasal
Oração WH
Preposição final
Advérbios
Subordinação condicional
Adjetivo em posição
atributiva
Preposição
Razão Forma-Ocorrência
Tamanho de palavra
Substantivo
Oração adjetiva reduzida de
gerúndio
Oração adjetiva reduzida de
particípio
Voz passiva sem agente
Advérbio de lugar
Verbo no tempo passado
Verbo no aspecto perfeito
Pronome de terceira pessoal
Verbo público
Negação sintética
Oração reduzida de gerúndio
Tamanho de palavra
Oração adjetiva reduzida de
particípio
Adjetivo em posição
Etiqueta
prv_vb
that_del
contrac
pro2
pres
pro_do
CountTags

















0.96
0.91
0.9
0.86
0.86
0.82
0.78
0.76
0.74
0.74
0.71
0.71
0.66
0.66
0.62
0.58
0.56
pdem
gen_emph
pro1
be_state
it
prtcle
sub_cos
pany
gen_hdg
amplifr
0.55
0.52
0.5
0.48
0.47
0.43
0.42)
0.32)
wh_ques
pos_mod
o_and
wh_cl
fnlprep
advs
sub_cnd
-0.47
adj_attr

-0.54
-0.54
-0.58
-0.8
-0.32)
prep
ttr
wrlength
n









-0.38)
-0.39)
-0.42)
agls_psv
pl_adv
0.9
0.48
0.43
0.43
0.4
0.39
pasttnse
perfects
pro3
pub_vb
-0.31)
-0.34)
wrlength
-0.41)
adj_attr




4
(Present tense verbs
Dimensão 3
Polo positivo
WH relative clauses on
object position
Pied piping constructions
WH relative clauses on
subject position
Nominalizations
Phrasal coordination
Polo negativo
Time adverbials
Place adverbials
Adverbs
Dimensão 4
Polo positivo (único)
Infinitives
Prediction modals
Suasive verbs
Conditional subordination
Necessity modals
Split auxiliaries
(Possibility modals
Dimensão 5
Polo positivo
Conjuncts
Agentless passives
Past participal clauses
BY-passives
Past participial WHIZ
deletions
Other adverbial
subordinators
(Predicative Adjective
Polo negativo
(Type-token ratio
atributiva
Verbos no tempo presente
Oração WH em posição de
objeto
Oração WH com preposição
inicial
Oração WH em posição de
sujeito
Nominalização
Coordenação frasal
Advérbio de tempo
Advérbio de lugar
Advérbios
Verbo no infinitivo
Verbo modal de antecipação
Verbo suasivo
Conjunção subordinativa -condicional
Verbo modal de necessidade
Advérbio encaixado no
auxiliar
Verbo modal de possibilidade
Conjuntivos
Voz passiva sem agente
Orações adjetivas reduzidas
de particípio
Voz passiva com preposição
BY
Modificador pós-nominal
passivo
Outros subordinativos
Adjetivo em posição
predicativa
Razão Forma-Ocorrência
-0.47)
pres
0.63
rel_obj

0.61
rel_pipe

0.45
rel_sub

0.36
0.36
n_nom
p_and


-0.46
-0.49
-0.6
tm_adv
pl_adv
advs



0.76
0.54
0.49
0.47
inf
prd_mod
sua_vb
sub_cnd



0.46
0.44
nec_mod
spl_aux


0.37)
pos_mod
0.48
0.43
0.42
conjncts
agls_psv



0.41
by_pasv

0.4
whiz_vbn

0.39
sub_othr
0.31)
pred_adj
-0.31)
ttr
5
6
7
8
9
Biber, D., & Conrad, S. (2009). Register, genre, and style. Cambridge
; New York: Cambridge University Press.
10
Working with tagged texts: The Biber tagger
5 ^zz++++=5
dead ^jj++++=dead,
, ^zz++++=EXTRAWORD
21 ^cd++++=21
wounded ^jj+++xvbn+=wounded
in ^in++++=in
holiday ^nn++++=holiday
shootings ^nns+++??+=shootings
across ^in+pl+++=across
city ^nn++++=city
Girl ^nn++++=Girl,
, ^zz++++=EXTRAWORD
10 ^cd++++=10,
, ^zz++++=EXTRAWORD
now ^rn+dspt+++=now
' ^zz++++='smiling
smiling ^jj+++xvbg+=EXTRAWORD
and ^cc+phrs+++=and
laughing ^jj+++xvbg+=laughing'
' ^zz++++=EXTRAWORD
after ^cs+sub+++=after
being ^xvbg+++xvbg+=being
shot ^nn+++xvbn+=shot
playing ^vwbg+++xvbg+=playing
in ^in++++=in
hydrant ^nn+++??+=hydrant
11
Text Scores
How to calculate:
Para cada texto, calcular o escore de fator, somando as frequências
normalizadas das características que compõe cada polo, separadamente, e em
seguida subtraindo a soma referente ao polo negativo do total do polo positivo.
Por exemplo, se no polo positivo de uma dimensão houver as características
substantivo e preposições, e no negativo verbos e pronomes pessoais, então o
escore de fator de cada texto será calculado pela fórmula (frequência
padronizada de substantivos + frequência padronizada de preposições) –
(frequência padronizada de verbos + frequência padronizada de pronomes
pessoais). Assim, um texto que tenha as frequências padronizadas iguais a
substantivo = 3,5, preposições = 2,0, verbos = 2,5, pronomes pessoais = 1,0,
terá um escore de fator na dimensão igual a 2,0, pois (3,5 + 2,0) – (2,5 + 1,0)
= 5,5 – 3,5 = 2,0.
Look in the file dim_scores_pucspmac.xlsx for some actual scores of the PUCSP
Metaphor Annotated Corpus (PUCSPMAC). What patterns can you identify with respect
to the distribution of scores on the dimensions and how they vary across registers?
PUCSPMAC composition
12
Working on the interaction vs informational dimension (#1).
Why is this dimension important? It is stable across languages; it is the most
significant of all dimensions. A translation can have a major impact on a text score on
this dimension, e.g. by reducing the interactive features and increasing the informational
features (or maybe the other way around, although that’s less likely?).
Features marked with < > below are the most characteristic on a dimension pole.
How would you translate the samples below? Do the relevant dimension features
change significantly in the translation or do they hold?
Dimension 1, positive pole
(1)
Register = Conversation
Score = 52.01
Text = -------- sbc050.trn.clean ---------I <pro1> thought they were gonna <contrac> be <pres> back by now.
You <pro2> did n't <contrac> hear <pres> <prv_vb> <that_del> them playing last
<advs> night .
I <pro1> know <pres> <prv_vb> .
Where <wh_ques> did they go <pres> ?
They went out to dinner with Arianna 's parents .
Arianna 's parents . Yeah . That <pdem> was her grandma on the phone .
They left at <advs> like <fnlprep> , quarter of eight . Mm . Maybe <gen_hdg> they
went shopping . First <advs> , and <o_and> then went to dinner .
I <pro1> think <pres> <prv_vb> <that_del> they 're <contrac> hanging out .
13
(2)
Register = Fiction
Score = 16.97
Text = -------- fifty_shades_of_grey.txt ---------I <pro1> scowl with frustration at myself in the mirror . Damn my hair ' it just
<gen_emph> will n't <contrac> behave <pres> , and <o_and> damn Katherine Kavanagh
for being ill and subjecting me <pro1> to this ordeal . I <pro1> should be <pres>
studying for my final exams , which are <pres> next week , yet <advs> here I <pro1> am
<pres> trying to brush my hair into submission . I <pro1> must not sleep <pres> with it
wet . I <pro1> must not sleep <pres> with it wet . Reciting this mantra several times , I
<pro1> attempt <pres> , once more , to bring it under control with the brush .
Negative pole
(3)
Register = news
Score = -25
Text = -------- news_000011.txt ---------Five men <n> were killed <agls_psv> and at <prep> least 21 were wounded
<agls_psv> in <prep> separate shootings <n> from <prep> Wednesday <n> morning <n>
to <prep> early <adj_attr> Thursday <n> . The first fatality <n> occurred about <prep>
10:30 a.m . Wednesday <n> , when Robert <n> Snipes <n> , 31 , was shot <agls_psv> in
<prep> the arm <n> and back during <prep> an argument <n> with <prep> another man
<n> in <prep> the 1700 block <n> of <prep> North <n> <pl_adv> Pulaski <n> Road <n>
, authorities <n> said . Snipes <n> , of <prep> the 200 block <n> of <prep> North <n>
<pl_adv> Kostner <n> Avenue <n> , was taken <agls_psv> to Mount Sinai <n> Hospital
14
<n> , were he later died . At 11:20 p.m . , a 35 - year <n> - old <adj_attr> man <n> was
found <agls_psv> dead with <prep> a gunshot <n> wound to <prep> the head <n> in
<prep> the 100 block <n> of <prep> East <n> <pl_adv> 68th Street <n> , according
<prep> to <prep> police.
(4)
Register = academic
Score = -24.43
Text = -------- chromosome.txt ---------Genome <n> scan <n> of <prep> human <adj_attr> systemic <n> lupus <n>
erythematosus <n> : Evidence <n> for <prep> linkage <n> on <prep> chromosome <n>
1q in <prep> African - american <adj_attr> pedigrees <n> Systemic <n> lupus <n>
erythematosus <n> ( sle <n> ) is an autoimmune <n> disorder <n> characterized <n> by
<prep> production <n> of <prep> autoantibodies <n> against <prep> intracellular <n>
antigens <n> including <prep> Dna <n> , ribosomal <n> P , Ro <n> ( ss <n> - a ) , La (
ss <n> - b ) , and the spliceosome <n> . Etiology <n> is suspected <agls_psv> to involve
genetic <adj_attr> and environmental <adj_attr> factors <n> . Evidence <n> of <prep>
genetic <adj_attr> involvement <n> includes : associations <n> with <prep> Hla <n> dr3 <n> , Hla <n> - dr2 <n> , Fc <n> receptors <n> ( fcr <n> ) Iia <n> and Iiia <n> , and
hereditary <adj_attr> complement <n> component <n> deficiencies <n> , as well as
familial aggregation <n> , monozygotic <n> twin <n> concordance <n>.
15