Focused C awling and Model E alua ion in he ield o
Con e sa ional Agen s and Mo i a ional In e iewing
Ge gana Roseno a Tsako a
Tu o s: Da id E. Losada Ca il y Ma cos Fe n´
andez Pichel
Mas e ’s Thesis, Uni e si y o San iago de Compos ela
Mas e in Massi e Da a Analisys Tecnologies: Big Da a
Abs ac —The exploi a ion o Mo i a ional In e iewing con-
cep s when analysing indi iduals’ speech con ibu es o gaining
aluable insigh s in o hei pe spec i es and a i udes owa ds
beha iou change. The sca ci y o labelled use da a poses
a pe sis en challenge and impedes echnical ad ancemen s
in esea ch in non-English language scena ios. To add ess
he limi a ions o manual da a labelling, we p opose a semi-
supe ised lea ning me hod as a means o augmen an exis ing
aining co pus. Ou app oach le e ages machine- ansla ed
use -gene a ed da a sou ced om social media communi ies
and employs sel - aining echniques o anno a ion. We con-
duc an e alua ion o mul iple classi ie s ained on a ious
augmen ed da ase s. To ha end, we conside di e se sou ce
con ex s and employ di e en e ec i eness me ics. The esul s
indica e ha his weak labelling app oach does no yield sig-
ni ican imp o emen s in he o e all classi ica ion capabili ies
o he models. Howe e , no able enhancemen s we e obse ed
o he mino i y classes. As pa o u u e wo k, we p opose
o enla ge he da ase s only wi h new examples om he
mino i y classes. We conclude ha se e al ac o s, including
he quali y o machine ansla ion, can po en ially bias he
pseudo-labelling models. The imbalanced na u e o he da a
and he impac o a s ic p e- il e ing h eshold a e o he
impo an aspec s ha need o be aken in o accoun .
1. In oduc ion
Heal h beha iou change is a di icul p ocess ha e-
qui es people o al e hei habi s and daily ou ine. Sus-
ained mo i a ion and de e mina ion a e undamen al o
achie ing ac ual change. Mo i a ional in e iewing (MI) is
a he apy app oach ha acili a es beha iou change by ex-
plo ing he language changes ha occu when an indi idual
unde goes ans o ma i e expe iences in hei li e [1]. The
p ima y goal o his app oach is o enhance indi iduals’
sel -awa eness ega ding hei mo i a ions o change and
o s eng hen hei pe sonal commi men owa ds achie ing
a goal.
Recen ad ances in Na u al Language P ocessing (NLP)
and A i icial In elligence (AI) ha e led o he widesp ead
a ailabili y and u ilisa ion o Con e sa ional Agen s (CAs),
sys ems wi h he capabili y o emula e human con e sa-
ions using ex o spoken language. Ex ensi e e idence has
demons a ed he po en ial ad an ages o u ilising CAs o
heal h- ela ed pu poses, suppo ing he p ocess o change
[2].
This wo k s ems om a collabo a i e p ojec be ween
Uni e sidad de San iago de Compos ela (USC) and Uni e -
si ¨
a Regensbu g (UR), which explo es he u ilisa ion o a
CA ha implemen s MI o enhance mo i a ion and os e
beha iou change. The CA employed in his p ojec en-
coun e s signi ican language limi a ions as ex ual esou ces
a e pa icula ly sca ce o non-English languages. In he
case o Ge man language, anno a ed MI da a a e comple ely
una ailable [2].
Sca ci y o use -gene a ed da a poses a signi ican chal-
lenge wi h a - eaching implica ions in domain-speci ic NLP
applica ions. The limi ed a ailabili y o labelled da a hinde s
o slows down he de elopmen o obus NLP models,
leading o po en ial limi a ions in hei pe o mance. In
such scena ios, esea che s o en ace limi ed ele an da a,
compelling hem o eso o cos ly and ime-consuming
app oaches o ad ance hei s udies. These app oaches in-
ol e labo ious collec ion and anno a ion o exis ing ex s
o op ing o ou -o -domain da a ha may no align wi h he
ask’s objec i es [3].
To mi iga e he esou ce-in ensi e na u e o adi ional
da a collec ion me hods, as e and mo e cos -e ec i e al e -
na i es a e o en explo ed o augmen he aining co po a.
Two p ominen al e na i es a e da a augmen a ion, which
in ol es applying a a ie y o ans o ma ions o he ex-
is ing labelled da a o c ea e syn he ic samples, and semi-
supe ised lea ning, an e icien solu ion ha le e ages unla-
belled da a, which is ypically mo e abundan han labelled
da a [4]. This is achie ed h ough echniques such as sel -
aining, whe e he model, ained on a limi ed amoun o
labelled da a, gene a es pseudo-labels o unlabelled da a.
These weakly labelled samples a e used o u he e ine he
model’s p edic ions. This app oach enables models o lea n
om a b oade ange o examples, enhancing gene alisa ion
and pe o mance.
Semi-supe ised lea ning ends o be e ec i e when we
ha e limi ed labelled da a bu ha e access o a la ge amoun
1
o unlabelled da a [4]. We s a om a small collec ion o
use -gene a ed ex da a in Ge man. This o iginal da ase
was anno a ed wi h labels ela ed o beha iou change. We
aim o explo e a iable way o augmen his labelled da ase .
Gi en he na u e o he p oblem a hand, labelling subjec ’s
u e ances, eal use -gene a ed da a is mo e aluable han
au oma ically gene a ed samples o syn he ic da a (e.g.,
de i ed om modi ying he o iginal a ailable da ase , using
augmen a ion echniques [5]). The exponen ial g ow h o
use -gene a ed con en published on In e ne , coupled wi h
he ac ha he o iginal aining da ase is sou ced om a
pee - o-pee online o um [6], a e ac o s in a ou o explo -
ing he po en ial o English-language online communi ies
and o ums as aluable sou ces o high-quali y use da a.
This wo k is he e o e guided by he ollowing esea ch
ques ion: A e machine- ansla ed use da a, sou ced om so-
cial media communi ies, and anno a ed ia semi-supe ised
lea ning, a iable solu ion o augmen an exis ing aining
co pus and o inc ease he base classi ie ’s pe o mance o
ca aloging beha iou change u e ances?
To add ess his ques ion, an exis ing human-labelled
Ge man-language da ase is used as baseline in his wo k.
This da ase applies u e ance codes de ined in he Mo i-
a ional In e iewing Skill Code (MISC) [7], which allows
concep ualiza ion o change- ela ed speech h ough he as-
signmen o alence, con en labels and sublabels.
Da a collec ed om online communi ies in English
language a e ini ially segmen ed in o sen ences. Nex , a
ans o me model [8] is u ilised o ansla e he segmen ed
da a in o Ge man. To ensu e he ele ance o da a, a p e-
il e classi ie , which has been ine- uned wi h on- opic and
o - opic sen ences, is used o iden i y sen ences ele an
o beha iou change1. Nex , semi-supe ised lea ning is
employed o p oduce weak labels, assembling mul iple new
da ase s om di e se beha iou change con ex s. To explo e
he iabili y o he semi-supe ised lea ning app oach, a
numbe o classi ica ion expe imen s a e conduc ed. Classi-
ie s a e ained on he newly cons uc ed da ase s and hei
pe o mance is e alua ed on a held-ou es se . In doing so,
we ob ain aluable insigh s abou he po en ial bene i s o
his me hod o add ess da a sca ci y in his speci ic domain.
2. Rela ed Wo k
This wo k alls wi hin he scope o an ongoing col-
labo a i e p ojec be ween he Uni e si y o San iago de
Compos ela (USC), Spain and he Uni e si y o Regensbu g
(UR), Ge many. In [6], he c ea o s o he o iginal collec ion,
named GLoHBCD, p o ided a ho ough explana ion on
he cons uc ion and e alua ion o his beha iou change
da ase . We aim a augmen ing his da ase and i s c ea o s
p o ided a de ailed me hodology o eplica ing he o iginal
expe imen s. This in ol es he c ea ion o h ee classi ie s
ac oss di e en beha iou change domains.
1. h ps://hugging ace.co/selmey/beha iou change p e il e ge man
2.1. Mo i a ional In e iewing and Beha iou
Change
Mo i a ional In e iewing (MI) is a clien -cen e ed ap-
p oach used in Psychology and Heal hca e o acili a e be-
ha iou change. I aims o elici mo i a ion in indi iduals
h ough goal-o ien ed communica ion o make changes in
hei li es. T adi ionally, he apis s employ a ious ech-
niques such as open ques ions, a i ma ions, e lec ions, and
summa ies o guide clien s owa ds change [1]. MI has ound
wide applica ion in domains like subs ance abuse ea men ,
weigh loss, and men al heal h in e en ions.
The inc eased adop ion o oice assis an s p esen s an
oppo uni y o con e sa ional agen s o suppo heal h man-
agemen . Ou esea ch ocuses on assis ing in he design o
pe suasi e CAs by applying MI concep s and echniques
[6]. Con e sa ional agen s employing MI echniques le e -
age na u al language p ocessing and machine lea ning al-
go i hms o unde s and and espond o use ’s inpu , sim-
ula ing human-like con e sa ion. Recen de elopmen s in
echnology-assis ed beha iou change inco po a e MI anno-
a ion echniques. The exploi a ion o he Mo i a ional In e -
iewing Skill Code helps o e alua e indi iduals’ u e ances
and o measu e he quali y and ideli y o MI in e en ions
[7].
P e ious esea ch ha e explo ed he c ea ion o au o-
ma ed counseling sys ems in which clien s in e ac wi h
an embodied con e sa ional agen ha ac s as a i ual
counselo [9], he de elopmen o agen -based in e en ions
o inc ease mo i a ion and con idence o p omo e physical
ac i i y [10] and he design o specialized CAs o suppo
pa en s’ s a egies ailo ed o heal hy ea ing goals [11]. The
e alua ion esul s o hese sys ems show p omising esul s.
Fo example, inc eased mo i a ion was obse ed in su eyed
indi iduals who had in e ac ions wi h he CAs. Howe e ,
he cons uc ion o CAs ailo ed o non-English speake s
emains la gely unexplo ed [6].
2.2. Semi-supe ised Lea ning and Da a Augmen-
a ion
Many s udies ha e a emp ed o au oma ically augmen
co po a in a ious ields, including compu e ision [12], au-
dio augmen a ion [13] and speech ecogni ion [14]. In hese
s udies, au oma ed da a augmen a ion esul ed in enhanced
pe o mance and mo e obus models, pa icula ly in scena -
ios whe e limi ed da a we e a ailable. Recen esea ch sug-
ges s ha his app oach applied o language da a could lead
o subs an ial imp o emen s in mul iple classi ica ion asks.
Va ious su eys [15], [16] p esen ed ex ual da a augmen a-
ion me hods such as synonym and embedding eplacemen ,
s uc u e-based ans o ma ions, sen ence eplacemen by
ound- ip ansla ion, and so o h. Da a augmen a ion in
he con ex o beha iou change u e ances was p e iously
explo ed [5] by eplacing and enhancing use da a wi h
syn he ic da a gene a ed by Cha GPT. The pe o mance o
he esul ing classi ie s was es ed on di e en combina ions
o syn he ic and eal use da a.
2
Da a augmen a ion ocuses on en iching he o iginal ex-
is ing da ase by in oducing syn he ic a ia ions. An al e na-
i e pa h o expand aining se s consis s o employing semi-
supe ised lea ning (SSL), which le e ages bo h labelled
and unlabelled da a. SSL is conce ned wi h si ua ions whe e
he e is a sca ci y o labelled da a bu an abundance o
unlabelled da a. SSL has eme ged as a popula me hod o
add essing da a sca ci y in deep lea ning con ex s, wi h ex
da a being a common domain o applica ion. Among he
a ious ypes o SSL, we ocus on sel - aining, which is
one o he pionee ing SSL app oaches and has demons a ed
s a e-o - he-a pe o mance in mul iple asks including neu-
al machine ansla ion [17].
The classic sel - aining me hodology in ol es employ-
ing a p e- ained classi ie o gene a e pseudo-labels o
unlabelled da a. These pseudo-labelled examples a e hen
combined wi h he o iginal co pus o c ea e an augmen ed
da ase , which is subsequen ly u ilized o e ain a new
model [18], [19]. In ecen yea s, di e en al e na i es ha e
been explo ed o imp o e sel - aining wi h weak supe i-
sion [20], egula iza ion [21], con as i e lea ning [22] and
consis ency lea ning [23].
Recen wo k on le e aging da a augmen a ion and
pseudo-labelling in he con ex o sleep- ela ed issues
showed p omising esul s [24]. Simila ly, [25] employed
SSL o label new da ase s wi h classi ie s ine- uned on he
GLoHBCD and in es iga ed he cha ac e is ics o w i en
language abou beha iou change.
3. Me hods
The p oposed me hodology in his wo k in ol es he
use o machine ansla ion on da a in English sou ced om
Reddi , he applica ion o sel - aining o gene a e pseudo-
labels and he e- aining o he o iginal models wi h he
goal o imp o ing he base classi ie s.
3.1. Da a ex ac ion
Wi h he inc easing popula i y o In e ne , indi iduals
o en sha e online hei expe iences and challenges ela ed
o men al heal h and beha iou change. This public exposu e
is suppo ed by a ious online pla o ms such as o ums and
blogs [26]. This ep esen s an oppo uni y o esea che s
o explo e and analyse abundan amoun s o use -gene a ed
da a, which can be exploi ed o eed machine and deep
lea ning algo i hms.
Reddi has gained an impo an ole in scien i ic esea ch
due o i s popula i y among a la ge and di e se use base. I s
communi ies, named sub eddi s, ocus on speci ic in e es s
and ha e a signi ican olume o publica ions. This allow
esea che s o a ge ele an scien i ic opics [27]. P e ious
s udies ha e sugges ed ha Reddi is a easible sou ce o
da a o c ea ing domain-speci ic aining da ase s, pa ic-
ula ly in he a ea o heal h. Fo example, Reddi da a has
been employed o de ec signs o anxie y o dep ession om
indi iduals’ in e ac ions [26], [27].
Reddi ’s publicly a ailable API acili a es he e ie al
and ex ac ion o use -gene a ed con en [28]. In addi ion,
Reddi is an app op ia e sou ce o da a o expand he GLo-
HBCD co pus. In ac , he e a e some simila i ies be ween
he pla o m used o cons uc GLoHBCD and Reddi , bo h
being pee - o-pee communica ion o ums whe e he con-
e sa ional s yle is indi ec among mul iple pa ies.
Six di e en sub eddi s, opic-speci ic online commu-
ni ies wi hin Reddi , we e u ilised o collec ing da a o
ou esea ch. Each one o hem is somehow ela ed o
beha iou change, bu co e s a speci ic opic. This allowed
o he o iginal co pus, ha is cen ed on weigh loss, o
be expanded wi h samples o beha iou change ocusing on
o he opics. The sub eddi s and opics a e he ollowing:
- /losei – heal hy me hods o lose weigh and main-
ain p og ess.
- /smokingcessa ion – encou agemen o qui smok-
ing and mo i a ion o hose who ha e al eady
s opped.
- /lea es – suppo o use s ying o s op d ug abuse.
- /s opd inking – mo i a ion o con olling o s op-
ping alcohol abuse.
- /sel imp o emen - inci ing change in all aspec s o
indi iduals’ li e.
- /DecidingToBeBe e – dedica ed o sel -
imp o emen .
Using Reddi ’s API, we ex ac ed a sample o housand
pos s o he “ op” ca ego y o each sub eddi ( op a ed pub-
lica ions). Acco ding o he pla o m’s o ing sys em, pos s
in he “ op” sec ion o a sub eddi a e highly ega ded by
he communi y. We assume such pos s a e o highe quali y
and pa icula ly use ul o pe o ming ou expe imen s. The
a e age leng h o a pos was 292 wo ds.
3.2. Da a p e-p ocessing
The a ailabili y o abundan use -gene a ed da a on he
web has led o subs an ial p og ess in di e se NLP asks.
Howe e , le e aging uns uc u ed da a is challenging and
equi es he usage o NLP ools o p e-p ocess he da ase s
be o e hey each he aining s age [26]. Fi s , we need o
segmen he pos s in o sen ences, as we wo k a sen ence-
le el in his p ojec . The a e age leng h o sen ences in
he co pus is 87 okens pe sen ence. Nex , sui able egula
exp ession ope a ions a e applied o emo e URLs, HTML
ags, special symbols, and emojis om he ex ual da a.
3.3. Machine T ansla ion wi h T ans o me s
A e he p e-p ocessing s age, da a need o go h ough
a machine ansla ion s age. Da a sca ci y is an omnip esen
issue in many NLP applica ions, especially in non-English
language p ojec s. As Mo i a ional In e iewing is ele an
o indi iduals ac oss he globe, i is c ucial o design ech-
nological solu ions o languages such as Ge man, which is
he a ge language o ou p ojec .
3
Figu e 1. P oposed semi-supe ised lea ning pipeline.
P e ious s udies sugges ha da a augmen a ion h ough
machine ansla ion is a p omising echnique. [29] gene a ed
new da a by ansla ing an anno a ed co pus om English o
U du o ake news de ec ion and [30] p oceeded he same
way be ween English and F ench o a eading comp e-
hension ask. O he ecen app oaches employed ze o-sho
mul ilingual MT echniques o imp o e end- o-end speech
ansla ion models [31] o machine ansla ion in he con ex
o speech ecogni ion o elephone con e sa ions [32].
The English-Ge man ansla ions ep esen a necessa y
s ep in he p oposed SSL pipeline and p oduce da a in a
a ge language ha aces da a sca ci y p oblems. Howe e ,
he success ul exploi a ion o da a in widesp ead languages
like English elies on he quali y o he ansla ion module.
Thus, a p ima y issue we seek o answe in ou expe imen s
is whe he o no he p esen quali y o ansla ions o he
English-Ge man language pai is good enough.
To ca y ou he MT an OPUS-MT p e- ained model
de eloped by he Language Technology Resea ch G oup
a he Uni e si y o Helsinki was employed. The model is
based on s a e-o - he a ans o me -based neu al machine
ansla ion (NMT) and ained on eely a ailable pa allel
co po a collec ed in he OPUS eposi o y [33]. The Reddi
co pus was ansla ed sen ence by sen ence and we made
no manual pos -edi ion (as i would be as cos ly as manual
ansla ion, which is a labo ious e o ha we a e ying o
a oid).
3.4. P e- il e ing
The semi-supe ised app oach we aim o apply equi es
in-domain unlabelled da a. To p omo e he inco po a ion
o ele an new da a, we choose as sou ces a ew sub ed-
di s ela ed o beha iou change, ansla e he publica ions
in o Ge man, apply a Ge man BERT language model and,
addi ionally, un a p e- il e ing s age. This s ep classi ies
TABLE 1. Size o sub eddi da ase s be o e and a e p e- il e ing.
sub eddi Size change ela ed % change ela ed
losei 25226 6135 24
smokingcessa ion 6747 1332 20
lea es 12475 1884 15
s opd inking 13628 1955 14
sel imp o emen 16105 1848 11
DecidingToBeBe e 14669 2279 16
each in-domain Ge man ex as ei he ela ed o no ela ed
o beha iou change. Acco ding o p e ious s udies, he
mac o F1 e ec i eness o his opic classi ie is 72.67%
[34]. This s ep enables he iden i ica ion o on- opic sen-
ences ha could be used o in e in o ma ion abou use s’
change beha iou . A s ic 0.99 con idence h eshold on
hese ele ance p edic ions is es ablished. Examples ha do
no su pass he con idence h eshold a e disca ded, educing
he da a o ins ances classi ied as “Change Rela ed” wi h
e y high con idence. The p e- il e ing model was ine- uned
on a da ase ela ed o weigh loss, and i exhibi s a endency
o iden i y a educed numbe o opic- ela ed sen ences when
applied o da a om o he domains. Table 1 epo s he size
o each sub eddi collec ion be o e and a e his ele ance
il e ing.
3.5. Base classi ie s
To build he o iginal base classi ie , he i s s ep consis s
o a ine- uning s ep on he o iginal da ase , GloHBCD. Glo-
HBCD is an exis ing co pus composed o Ge man-language
ex s abound beha iou change. The ex ac s a e anno a ed
wi h con en ca ego ies and alences based on he MISC
codes [7]. Each sen ence ep esen s a pe son’s u e ance
a ound change, and i is anno a ed wi h one alence label
(“+” o change alk and “-” o sus ain alk). In addi ion o
he alences, he sen ences a e assigned one o h ee possi-
4
TABLE 2. Tes se pe o mances (%) o baseline classi ie s.
Tes se
Accu acy Mac o F1 P ecision Recall
Valence 75.97 69.79 71.53 68.84
Label 84.50 77.31 79.37 75.72
Sublabel 80.54 73.68 71.77 76.15
ble con en labels: Reason (R) ha encompasses he basis,
incen i es, jus i ica ion o mo i es o change, Taking S eps
(TS) ep esen ing speci ic s eps ha ha e been aken owa ds
change and Commi men (C) ha includes ag eemen , in en-
ion, o obliga ion ega ding u u e beha iou . The ins ances
in he Reason ca ego y addi ionally ecei e one o ou
addi ional sublabels, indica ing he na u e o he eason
o change: gene al (R ) ep esen s he examples wi h no
sublabel, abili y (Ra) encompasses abili y and deg ee o
di icul y o he change, desi e (Rd) being desi e o will and
need (Rn) ep esen s need o necessi y. All hese labels we e
assigned manually, ollowing an anno a ion scheme d i en
by keywo ds [6].
GLoHBCD was sepa a ed in o h ee aining se s, each
one co esponding o one le el o classi ica ion: alence,
con en label o eason sublabel. Th ee sepa a e base classi-
ie s we e de eloped by ine- uning a p e- ained Ge man
BERT base model o each classi ica ion le el. BERT, a
deep bi-di ec ional ans o me , enables he cons uc ion o
e ec i e classi ica ion models in mul iple ex classi ica ion
asks, especially in he medical ield [35].
The es se s we e c ea ed by using a 80/20 andom
s a i ied spli . The ine- uning was pe o med using 10- old
c oss alida ion ac oss h ee epochs. The esul ing models
a e hen used o make p edic ions on he es se s.
The es se con ains 929 examples o alence and label.
Mac o-a e aged F1 is an app op ia e measu e because he
da ase s a e imbalanced. This me ic is mo e sui able o
e alua ing he models han Mic o-F1, because i e lec s he
ue model pe o mance e en when he classes a e skewed.
We also epo classi ica ion accu acy and a e aged ecall
and p ecision. Table 2 epo s he pe o mance o he h ee
base classi ie s.
In addi ion, we will some imes epo F1, p ecision, and
ecall o each class sepa a ely. These me ics a e de ined
as ollows:
P ecision =T P
T P +F P (1)
Recall =T P
T P +F N (2)
F1 = 2·P ecision ·Recall
P ecision +Recall (3)
whe e TP, FP, and FN ep esen ue posi i es, alse
posi i es, and alse nega i es o each class, espec i ely.
TABLE 3. Dis ibu ion o labels a e assigning pseudo-labels.
Valence Con en Label Reason Label
sub eddi %- %+ %R %TS %C %gene-
al %a %d %n
losei 17 83 60 32 7 74 13 9 4
smokingcessa ion 27 73 70 23 7 68 15 13 4
lea es 25 75 75 18 7 70 16 11 4
s opd inking 24 76 66 25 9 74 14 9 3
sel imp o emen 25 75 69 22 9 67 19 9 5
DecidingToBeBe e 19 81 64 25 12 68 16 12 5
3.6. Pseudo-labelling
We aim o e ec i ely augmen he aining se s o he
h ee base classi ie s, ollowing he me hodology used in
[20] and [36]. The nex s ep in he sel - aining app oach
is o use he base models o gene a e pseudo-labels o he
unlabelled da a. The ine- uned base models a e employed
o anno a e he ansla ed in-domain sen ences, ob aining
weakly labelled ins ances. Pseudo-labelling equi es mul i-
ple aining sessions, howe e ecen wo k sugges s ha he
mos e icien scena io is o conduc pseudo-labelling only 1-
2 imes [24], hus we chose o do only one i e a ion. Table 3
shows label dis ibu ion a e applying pseudo-labelling.
3.7. Augmen ing he aining se s
To alida e he p oposed me hod, a ious expe imen s
wi h di e en da ase s we e conduc ed. The main goal o
hese expe imen s was o e alua e o wha ex en SSL can
con ibu e o enhance he pe o mance o he o iginal clas-
si ie s. To ha end, he o iginal pe o mance o he base
models was compa ed o he pe o mance o each model
a e being e- ained wi h new da a.
We combine he ins ances om he o iginal aining
da ase , GLoHBCD, wi h he newly labelled Reddi da a,
wo king wi h each sub eddi sepa a ely. Following [24],
we es ablish h ee di e en con idence h esholds o he
pseudo-label p edic ion: 0.5, 0.75 and 0.99. We only include
new examples, wi h a posi i e o nega i e label, ha a e
classi ied wi h a con idence sco e highe han he h eshold.
The examples classi ied wi h low con idence a e he e o e
igno ed. No e ha wi h he 0.5 h eshold all examples a e
inco po a ed (wi h ei he posi i e o nega i e pseudo-label).
The di e en sub eddi s complemen he base aining
se in di e en ways by in eg a ing di e se opics. We
belie e ha such di e si y will inc ease he gene alisa ion
abili ies o he models and inc ease hei pe o mance.
Besides es ing he inco po a ion o ins ances om each
indi idual sub eddi , we also es a mixed con igu a ion
whe e new ins ances come om all sub eddi s. This leads
o 21 di e en a ian s –(6 sub eddi s + all sub eddi s) * 3
con idence h esholds– applied on each base classi ie . As
he expe imen s a e conduc ed o h ee classi ica ion asks,
his esul s in a o al numbe o 63 new aining se s.
Table 4 shows he dis ibu ion o pseudo-labels assigned
by he base classi ie o he da a ex ac ed om each sub-
eddi .
5
TABLE 4. O e iew o augmen ed da ase s and label dis ibu ions.
Valence Label Sublabel
T aining se h eshold size % - % + size %R %TS %C size %R %Ra %Rd %Rn
GLoHBCD - 3703 31 69 3696 65 25 10 2411 69 16 9 6
losei
50 9838 22 78 9831 62 30 8 6120 72 14 9 5
75 9683 22 78 9534 63 29 8 5924 73 14 9 5
99 8788 20 80 8182 65 27 8 4903 78 11 8 4
smokingcessa ion
50 5035 30 70 5028 66 25 9 3345 68 16 10 6
75 4979 29 71 4943 67 24 9 3286 69 15 10 6
99 4738 28 72 4677 67 24 9 3018 71 14 10 6
lea es
50 5587 29 71 5580 68 23 9 3828 69 16 10 5
75 5526 29 71 5499 69 23 8 3728 69 15 10 5
99 5208 28 72 5178 70 22 8 3321 73 13 9 5
s opd inking
50 5658 28 72 5651 65 25 9 3697 70 15 9 5
75 5599 28 72 5557 66 25 9 3625 71 15 9 5
99 5266 27 73 5186 66 24 9 3303 73 13 9 5
sel imp o emen
50 5551 29 71 5544 66 24 9 3684 68 17 9 6
75 5503 28 72 5428 67 24 9 3587 69 16 9 6
99 5211 27 73 5104 68 23 9 3214 71 14 9 6
DecidingToBeBe e
50 5982 26 74 5975 65 25 10 3862 68 16 10 6
75 5931 26 74 5858 65 25 10 3773 69 15 10 6
99 5626 25 75 5429 66 24 10 3351 72 13 9 6
mixed
50 19136 23 77 19129 65 26 9 12481 70 15 10 5
75 18706 22 78 18339 66 26 8 11868 72 14 10 4
99 16322 19 81 15276 69 23 8 9055 79 9 8 3
The da ase sizes di e based on he sub eddi used
o augmen he o iginal co pus, wi h an a e age o 6010
examples o alence and label, nea ly double he size o
he GLoHBCD da ase . This esul s in app oxima ely hal
he se s being composed o labelled da a and he o he
hal o unlabelled da a. As pe he c ea ion o he sublabel
da ase s, all examples anno a ed as Reason (R), 69,5% o
he da a on a e age, we e aken in o conside a ion. The
sublabel da ase s a e smalle , a e aging 3865 examples, in
con as o he o iginal sublabel da ase , which consis s o
2411 examples. All hese s a is ics do no ake in o accoun
he mixed da ase , which is assembled by inco po a ing
examples om all sub eddi s.
All da ase s a e imbalanced, mi o ing he pa e n ob-
se ed in he o iginal da ase s. The dis ibu ion o alence,
labels and sublabels ac oss da ase s is simila , which sug-
ges s ha he way use s add ess beha iou change in w i en
language emains consis en ega dless o he con ex . A
highe h eshold leads o a highe pe cen age o examples
o he majo i y class.
3.8. Re- aining
We pe o m classic sel - aining by inco po a ing he
pseudo-labelled da a wi hou implemen ing any addi ional
ans o ma ion. Unde his app oach, a BERT Ge man-cased
language model is ine- uned om each o he newly con-
s uc ed aining se s. This aining p ocess is exac ly he
same as he o iginal aining done o build he base models.
I ollows a 10- old c oss alida ion o e h ee epochs, we
do no change he hype -pa ame e se ings ei he .
4. Expe imen al esul s
We conduc ed sepa a e expe imen s on all label-le els,
employing di e en base classi ie s, and compa ing he
newly ob ained SSL-based classi ie s wi h he o iginal clas-
si ie s. Addi ionally, we in es iga e how he con idence
h esholds in luence model’s pe o mance. Ou indings e-
eal ha he new models p oduce a ying e ec s depending
on he classi ica ion ask conside ed. Speci ically, we ob-
se ed sligh imp o emen s in e ec i eness o he alence
and sublabel classi ie s, while he label classi ie s exhibi
a decline in pe o mance when pseudo-labelled da a a e
in oduced.
4.1. Valence
Fo he alence le el o classi ica ion, we ace a bi-
na y classi ica ion scena io. The model assigns a posi i e
o nega i e label o he indi iduals’ speech. The mino i y
class is “sus ain” (o nega i e) and he majo i y class is
“change”, which is ep esen ed by he posi i e label. The
esul s a e shown in Table 5. A modes o e all imp o emen
o app oxima ely 1-2% is obse ed ac oss da ase s. Gen-
e ally, classi ie s ained on da ase s wi h highe (s ic e )
h esholds yield be e pe o mance. No ably, he e is a sig-
ni ican imp o emen in pe o mance in he mino i y class.
The sub eddi s abou alcohol and d ug abuse and gene al li e
changes (s opd inking,lea es,DecidingToBeBe e ) a e he
mos p omising, as we ob ain he e he highes pe o mance
esul s (da ase s DecidingToBeBe e 99 and lea es 75).
4.2. Label
In he case o he label classi ie , we add ess a mul i-
class classi ica ion scena io wi h h ee classes: Reason (R)
6
TABLE 5. Resul s o alence classi ie .
Valence
Accu acy F1-sco e P ecision Recall
T aining se h a g - + a g - + a g - +
GLoHBCD - 75.97 69.79 56.13 83.46 71.53 62.83 80.23 68.84 50.71 86.96
losei
50 75.54 69.32 55.51 83.13 70.94 61.84 80.03 68.42 50.36 86.49
75 75.43 70.21 57.73 82.68 70.79 60.31 81.26 69.76 55.36 84.16
99 76.84 71.56 59.32 83.81 72.56 63.41 81.71 70.87 55.71 86.02
smokingcessa ion
50 77.38 71.67 58.94 84.39 73.40 65.50 81.29 70.65 53.57 87.73
75 75.54 70.31 57.84 82.77 70.92 60.55 81.29 69.84 55.36 84.32
99 77.16 71.80 59.50 84.10 73.01 64.32 81.70 71.00 55.36 86.65
lea es
50 75.87 70.06 56.87 83.25 71.33 62.03 80.64 69.26 52.50 86.02
75 77.49 72.17 60.00 84.34 73.44 65.00 81.87 71.34 55.71 86.96
99 75.65 69.72 56.31 83.12 71.05 61.70 80.41 68.91 51.79 86.02
s opd inking
50 77.06 71.43 58.75 84.11 72.92 64.53 81.30 70.52 53.93 87.11
75 76.19 70.83 58.33 83.33 71.73 62.10 81.36 70.20 55.00 85.40
99 77.06 71.57 59.07 84.06 72.89 64.29 81.49 70.72 54.64 86.80
sel imp o emen
50 75.76 69.74 56.25 83.23 71.21 62.07 80.35 68.88 51.43 86.34
75 75.76 70.82 58.82 82.82 71.21 60.61 81.82 70.50 57.14 83.85
99 76.52 70.58 57.37 83.79 72.24 63.76 80.72 69.63 52.14 87.11
DecidingToBeBe e
50 75.76 69.81 56.42 83.21 71.20 61.97 80.43 68.98 51.79 86.18
75 75.65 69.72 56.31 83.12 71.05 61.70 80.41 68.91 51.79 86.02
99 77.49 72.17 60.00 84.34 73.44 65.00 81.87 71.34 55.71 86.96
mixed
50 76.19 70.42 57.36 83.48 71.76 62.71 80.81 69.60 52.86 86.34
75 75.87 70.54 58.00 83.07 71.32 61.35 81.28 69.97 55.00 84.94
99 75.87 70.27 57.36 83.17 71.32 61.73 80.91 69.57 53.57 85.56
TABLE 6. Resul s o label classi ie .
Label
Accu acy F1-sco e P ecision Recall
T aining se h a g R TS C a g R TS C a g R TS C
GLoHBCD - 84.50 77.31 90.21 75.45 66.28 79.37 87.27 82.18 68.67 75.72 93.36 69.75 64.04
losei
50 83.42 76.68 89.16 73.80 67.07 79.02 86.91 76.82 73.33 74.78 91.53 71.01 61.80
75 83.96 77.20 89.16 75.77 66.67 80.55 86.31 79.63 75.71 74.67 92.19 72.27 59.55
99 83.96 76.28 89.53 76.27 63.03 78.68 86.88 80.75 68.42 74.35 92.36 72.27 58.43
smokingcessa ion
50 83.42 76.33 89.07 74.44 65.48 78.57 86.29 79.81 69.62 74.52 92.03 69.75 61.80
75 82.02 73.87 88.67 71.49 61.45 75.81 86.44 74.77 66.23 72.27 91.03 68.49 57.30
99 83.42 76.86 89.00 73.76 67.84 78.93 86.16 79.90 70.73 75.23 92.03 68.49 65.17
lea es
50 84.39 75.96 90.37 76.17 61.35 78.68 87.42 81.04 67.57 73.85 93.52 71.85 56.18
75 82.88 74.45 89.34 73.41 60.61 76.57 86.95 76.96 65.79 72.74 91.86 70.17 56.18
99 83.75 76.51 89.46 74.21 65.85 79.52 86.15 80.39 72.00 74.20 93.02 68.91 60.67
s opd inking
50 83.32 76.05 89.14 75.05 63.95 77.16 87.64 77.58 66.27 75.06 90.70 72.69 61.80
75 84.39 77.91 89.85 75.59 68.29 80.12 87.92 77.78 74.67 76.10 91.86 73.53 62.92
99 83.21 75.37 89.23 73.47 63.41 78.33 85.87 79.80 69.33 73.12 92.86 68.07 58.43
sel imp o emen
50 83.75 75.82 89.55 74.94 62.96 79.00 86.18 80.98 69.86 73.41 93.19 69.75 57.30
75 83.10 75.65 88.96 74.25 63.75 78.23 86.98 75.88 71.83 73.67 91.03 72.69 57.30
99 83.42 76.57 88.94 74.11 66.67 79.64 85.91 79.05 73.97 74.20 92.19 69.75 60.67
DecidingToBeBe e
50 83.10 75.64 89.21 73.48 64.24 77.68 87.16 76.13 69.74 73.97 91.36 71.01 59.55
75 83.96 77.00 89.85 73.26 67.90 80.23 86.59 78.74 75.34 74.55 93.36 68.49 61.80
99 83.53 75.91 89.55 73.94 64.24 78.39 86.76 78.67 69.74 73.94 92.52 69.75 59.55
mixed
50 84.39 77.16 89.59 77.29 64.60 79.93 87.13 80.45 72.22 75.00 92.19 74.37 58.43
75 82.78 74.50 89.18 73.50 60.82 76.14 86.79 78.20 63.41 73.15 91.69 69.33 58.43
99 82.99 75.24 88.60 75.38 61.73 77.69 86.30 78.28 68.49 73.30 91.03 72.69 56.18
as he majo i y class, and Taking s eps (TS) and Commi -
men (C) as he mino i y classes. The esul s a e shown in
Table 6. Con a y o he obse a ions made wi h he alence
classi ie , we do no obse e he e an o e all imp o emen
in pe o mance. Only wi h he s opd inking da ase and a
h eshold o 0.75, we ound a ma ginal imp o emen o
less han 1%, which we conside insigni ican . On a e age,
hese classi ie s pe o m one pe cen age poin lowe han
he baseline classi ie . The e appea s o be an inc ease in
he e ec i eness me ics o he mino i y classes, bu his
imp o emen comes a he cos o a decline in he majo i y
class. The mos subs an ial imp o emen is obse ed in he
Commi men class, which exhibi s inc eases in p ecision
and ecall in he ange o 5%-7%. Di e en h esholds do
no signi ican ly impac on he esul s. The highes igu es
a e achie ed when using da a om sub eddi s lea es and
s opd inking wi h a h eshold 0.75.
4.3. Sublabel
The ask o sublabel classi ica ion consis s o mul ilabel
classi ica ion wi h ou labels, see Table 7. The subla-
bel classi ie s exhibi beha iou ha is simila o he one
achie ed by he label classi ie s. The esul ing Mac o F1
a ies by app oxima ely ±1%, al hough we do no claim
s a is ical signi icance o his di e ence. No ably, i is ob-
se ed an imp o emen in F1 and ecall o he majo i y class
7
TABLE 7. Resul s o sublabel classi ie .
Label
Acc. F1-sco e P ecision Recall
T aining se h a g R Ra Rd Rn a g R Ra Rd Rn a g R Ra Rd Rn
GLoHBCD - 80.54 73.68 86.60 61.29 75.59 71.23 71.77 88.58 60.64 67.61 70.27 76.15 84.71 61.96 85.71 72.22
losei
50 78.19 71.31 84.75 56.38 73.85 70.27 68.97 87.37 55.21 64.86 68.42 74.46 82.28 57.61 85.71 72.22
75 79.03 71.70 85.68 55.06 75.00 71.05 69.58 87.19 56.98 66.67 67.50 74.55 84.22 53.26 85.71 75.00
99 79.36 71.91 85.89 54.14 80.00 67.61 70.74 86.85 55.06 72.46 68.57 73.54 84.95 53.26 89.29 66.67
smokingcessa ion
50 80.54 73.88 86.63 58.38 78.05 72.46 73.26 87.59 58.06 71.64 75.76 74.88 85.68 58.70 85.71 69.44
75 80.37 72.60 86.76 59.09 75.20 69.33 71.08 87.62 61.90 68.12 66.67 74.65 85.92 56.52 83.93 72.22
99 80.20 73.40 86.21 58.24 78.74 70.42 72.06 87.50 58.89 70.42 71.43 75.32 84.95 57.61 89.29 69.44
lea es
50 80.03 74.71 85.68 58.51 78.69 75.95 71.94 87.98 57.29 72.73 69.77 78.08 83.50 59.78 85.71 83.33
75 81.21 74.73 87.06 57.47 78.33 76.06 74.58 86.75 60.98 73.44 77.14 75.16 87.38 54.35 83.93 75.00
99 80.54 73.73 86.73 57.14 80.00 71.05 72.02 87.81 57.78 75.00 67.50 75.73 85.68 56.52 85.71 75.00
s opd inking
50 80.87 74.24 86.90 57.78 79.03 73.24 73.27 87.65 59.09 72.06 74.29 75.60 86.17 56.52 87.50 72.22
75 81.21 74.92 86.94 58.43 80.33 73.97 73.79 87.47 60.47 74.24 72.97 76.36 86.41 56.52 87.50 75.00
99 80.20 73.03 86.48 55.37 80.00 70.27 71.97 86.80 57.65 75.00 68.42 74.34 86.17 53.26 85.71 72.22
sel imp o emen
50 80.54 73.58 86.86 54.55 79.67 73.24 72.91 87.07 57.14 73.13 74.29 74.64 86.65 52.17 87.50 72.22
75 79.03 72.01 85.19 56.67 77.78 68.42 69.91 86.68 57.95 70.00 65.00 74.72 83.74 55.43 87.50 72.22
99 79.70 72.77 86.21 55.03 81.36 68.49 71.52 87.50 53.61 77.42 67.57 74.16 84.95 56.52 85.71 69.44
DecidingToBeBe e
50 80.03 73.13 85.96 58.24 81.67 66.67 71.15 87.25 58.89 76.56 61.90 75.51 84.71 57.61 87.50 72.22
75 80.03 73.27 86.00 58.76 77.27 71.05 70.91 87.85 61.18 67.11 67.50 76.70 84.22 56.52 91.07 75.00
99 81.04 73.29 87.29 54.76 80.67 70.42 73.60 86.26 60.53 76.19 71.43 73.38 88.35 50.00 85.71 69.44
mixed
50 79.36 72.09 85.78 52.94 78.74 70.89 69.97 86.63 57.69 70.42 65.12 75.23 84.95 48.91 89.29 77.78
75 79.03 71.93 85.57 53.93 77.17 71.05 69.82 86.97 55.81 69.01 67.50 74.72 84.22 52.17 87.50 75.00
99 78.69 72.46 84.97 53.97 80.65 70.27 70.39 87.02 52.58 73.53 68.42 74.99 83.01 55.43 89.29 72.22
(R ). One o he mino i y classes, Rd, ge s imp o emen s in
F1, p ecision, and ecall ac oss mul iple da ase s. Howe e ,
hese imp o emen s comes a he expense o a decline in
he emaining classes. The bes pe o ming classi ie s a e
hose ine- uned on da ase s om he lea es,s opsmoking
and DecidingToBeBe e sub eddi s. Once again, he esul s
indica e ha he con idence h eshold o he pseudo-labels
is no c ucial when i comes o he pe o mance on he es
se .
5. Discussion and u u e wo k
A e conduc ing a se ies o expe imen s o e machine-
ansla ed and pseudo-labelled da ase s, he ob ained esul s
sugges ha he p oposed app oach o combining MT and
SSL does yield mode a e imp o emen s in some speci ic
ins ances. I was obse ed a gene al end o imp o emen o
he mino i y class ac oss h ee di e en classi ica ion asks.
Al hough ce ain classes showed imp o emen s in indi idual
me ics, he gene al p edic i e powe o he SSL classi ie s
emained wi hin he ange o he pe o mance achie ed by
he baseline models. Va ious con idence h esholds o inco -
po a ing pseudo-labels we e explo ed, unde he hypo hesis
ha highe h esholds would esul in be e pe o mance.
Howe e , we obse ed a end whe e he a e age F1 sco e
ei he emained he same o sligh ly dec eased wi h a highe
h eshold. Fu he mo e, he bes esul s did no o en co -
espond wi h he highes , mo e s ingen , h esholds. These
indings sugges ha he con idence le el o he pseudo-label
does no ha e a s ong in luence on he p edic i e capabili y
o he SSL model.
Ano he impo an aspec is he aining se size. In gen-
e al, he new da ase s a e compa able o he o iginal ones,
excep om he mixed da ase s ha inco po a e pseudo-
examples om all sub eddi s. Howe e , he mixed classi ie s
did no yield be e esul s, despi e ha ing been ine- uned
wi h la ge da ase s. This esul implies ha he size o he
da ase does no play a signi ican ole in he classi ica ion
pe o mance.
The opic o he sub eddi used o ob ain he augmen ed
aining se appea s o play a c ucial ole in he classi ica-
ion imp o emen . The highes sco es we e ob ained om
da ase s o sub eddi s ocused on alcohol abuse, d ug usage
and gene al li e imp o emen s. These sub eddi s deal wi h
aspec s ha a e no ela ed o weigh -loss bu i seems ha
hey supply complemen a y u e ances abou li e change
ha a e e ec i e as addi ional signs o he classi ie s.
Addi ionally, i is wo h no ing ha he o iginal da ase is
de i ed om a o um whe e pa icipan s we e aiming o lose
weigh o we e in he p ocess o doing so. The sub eddi s
men ioned be o e include indi iduals who a e in he phase
o a emp ing o main ain he changes hey ha e achie ed,
hus placing hem in a di e en s age o beha iou change.
Fine- uning language models using SSL is challenging
due o he p esence o noisy labels. T adi ional sel - aining
mechanisms o e look he base model’s weakness du ing he
pseudo-labelling p ocess [37]. We a gue ha he e ec i e-
ness o he app oach is a ec ed by he obus ness o he ini-
ial models. In ou case, he base classi ie s we e ained wi h
limi ed da ase s, wi h 3700 samples only. Rela ed o his, we
obse ed he sca ci y o pseudo-labelled da a impu ed o he
mino i y classes. This p oduces addi ional da a imbalance in
he e- aining phase and esul s in poo pe o mance.
Ano he limi a ion ha mus be aken in o conside a ion
is he quali y o machine ansla ion. P e ious esea ch has
highligh ed he po en ial inaccu acies o machine ansla ion
[29], which may con ibu e o lowe ed pe o mance o he
BERT models on he es da a. Some inconsis encies can
be ound in he ansla ed da a, o example, he ansla ion
o ” as ” in English o ”schnell” (ins ead o he app op ia e
ansla ion ” as en” in he gi en con ex ). Mo eo e , MT
could al e he na u al wo d o de , esul ing in uncommon
8
combina ions o wo ds. This could be an issue o accu a ely
labelling use u e ances owa ds change. In he u u e, we
will u he analyse he MT module, compa e mul iple so-
lu ions and, possibly, inco po a e pseudo-labelled da a om
mul iple language sou ces.
In gene al, we obse ed p omising imp o emen s in
me ics o he mino i y classes. A po en ial SSL app oach
ha could be conside ed in u u e wo k consis s o includ-
ing only aining pseudo-labelled samples o he mino i y
labels. This could help o mi iga e da a imbalance and
ob ain highe o e all esul s. In addi ion, as pe o mance
depends on he sub eddi ’s opics, da a om o he (non-
heal h) change ela ed opics could be sou ced.
Ano he u u e line o esea ch could be o ien ed o
lowe ing he h eshold o he p e- il e ing classi ie . Such
an app oach would eed mo e examples o he subsequen
modules and i could help o acqui e a la ge numbe o
examples.
6. Conclusion
Limi ed labelled da a is a common issue in many ma-
chine lea ning applica ions. This wo k has add essed his
p oblem in he con ex o an ongoing p ojec cen ed a ound
a Con e sa ional Agen employing Mo i a ion In e iewing
echniques. Ou p oposed app oach aims o mi iga e he
sca ci y o MI anno a ed da a in Ge man language. To
ha end, we c ea ed a semi-supe ised lea ning pipeline,
consis ing o da a sc aping, machine ansla ion and sel -
aining o educe he cos ly and labo -in ensi e p ocess o
labelling da a manually. The objec i e was o de elop obus
classi ica ion models o anno a ing use s’ change- ela ed
speech acco ding o he MISC code. O ien ed o hese goals,
ou s udy consis ed o a se ies o expe imen s wi h p e-
ained ans o me -based models and h ee di e en classi-
ica ion asks.
We hypo hesised ha English language da a om he
pla o m Reddi om di e se opic could be ansla ed in o
Ge man and hen used o augmen a p e-exis ing Ge man
language co pus and, as a consequence, enhancing he p e-
dic i e accu acy o beha iou change u e ance de ec ion.
Howe e , he expe imen al esul s demons a ed ha his
app oach did no yield signi ican imp o emen s in he
classi ica ion capabili ies, compa ed o he baseline models.
Se e al ac o s migh con ibu e o hese indings, including
inaccu acies in he au oma ed ansla ion, po en ial biases
o he pseudo-labelling models due o imbalanced aining
da ase s, he p esence o noisy labels, and he es ablished
s ic p e- il e ing h eshold. Ou s udy illus a es he ype
o challenges encoun e ed in ex classi ica ion wi h non-
English languages and unde sco es he need o u he
esea ch in add essing hese limi a ions.
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