This work investigates to what degree speakers with different verbal intelligence may adapt to each other. The work is based on a corpus consisting of 100 descriptions of a short film (monologues), 56 discussions about the same topic (dialogues), and verbal intelligence scores of the test participants. Adaptation between two dialogue partners was measured using cross-referencing, proportion of "I", "You" and "We" words, between-subject correlation and similarity of texts. It was shown that lower verbal intelligence speakers repeated more nouns and adjectives from the other and used the same linguistic categories more often than higher verbal intelligence speakers. In dialogues between strangers, participants with higher verbal intelligence showed a greater level of adaptation

Fernández Martínez, Fernando

Minker, Wolfgang

Zablotskaya, Kseniya

Archivo Digital UPM

Estimating Adaptacion of Dialogue Partners with Different Verbal Intelligence

Servicio de Coordinación de Bibliotecas de la Universidad Politécnica de Madrid

Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pages 126–130,
Seoul, South Korea, 5-6 July 2012. c©2012 Association for Computational Linguistics
Estimating Adaptation of Dialogue Partners
with Different Verbal Intelligence
K.Zablotskaya
Inst. of Communications
Engineering,
University of Ulm, Germany
kseniya.zablotskaya@
uni-ulm.de
F.Ferna´ndez-Martı´nez
E.T.S.I. de Telecomunicacı´on,
Universidad Polite´cnica
de Madrid, Spain
ffm@die.upm.es
W.Minker
Inst. of Communications
Engineering,
University of Ulm, Germany
wolfgang.minker@
uni-ulm.de
Abstract
This work investigates to what degree speak-
ers with different verbal intelligence may
adapt to each other. The work is based on
a corpus consisting of 100 descriptions of a
short film (monologues), 56 discussions about
the same topic (dialogues), and verbal intelli-
gence scores of the test participants. Adapta-
tion between two dialogue partners was mea-
sured using cross-referencing, proportion of
“I”, “You” and “We” words, between-subject
correlation and similarity of texts. It was
shown that lower verbal intelligence speak-
ers repeated more nouns and adjectives from
the other and used the same linguistic cat-
egories more often than higher verbal in-
telligence speakers. In dialogues between
strangers, participants with higher verbal in-
telligence showed a greater level of adapta-
tion.
1 Introduction
When two speakers are talking to each other, they try
to adapt to their dialogue partner and synchronize
their verbal behaviours. The adaptation may occur
at different levels: lexical (Garrod and Anderson,
1987; Brennan and Clark, 1996), syntactic (Reitter
et al., 2006), acoustic (Ward and Litman, 2007), ar-
ticulation (Bard et al., 2000), comprehension (Lev-
elt and Kelter, 1982), etc. Moreover, synchroniza-
tion of dialogue partners at one level may cause the
adaptation process at any other level (Pickering and
Garrod, 2004; Cleland and Pickering, 2003). In this
paper we analyse to what degree dialogue partners
with different verbal intelligence and levels of ac-
quaintance may adapt to each other during a conver-
sation.
Verbal intelligence (VI) is “the ability to analyse
information and to solve problems using language-
based reasoning” (Logsdon, 2012). The ability to
find suitable words and expressions may be a great
help in accomplishing such goals as persuasions, en-
couragements, explanations, influence, etc. More-
over, there exists a dependency between an individ-
ual’s verbal intelligence level and his or her success
in life (Buzan, 2002).
The first hypothesis we check in this paper is that
both lower and higher verbal intelligence speakers
are able to adapt to their dialogue partners; how-
ever, this adaptation is reflected by different linguis-
tic features.
The second hypothesis we check in this work
is that when higher and lower verbal intelligence
speakers are talking to a stranger, the former ones
adapt better to their dialogue partner than the latter
ones.
This investigation may be helpful for improv-
ing the user-friendliness of spoken language dia-
logue systems. Systems which automatically adapt
to users’ language styles and change their dialogue
strategies may help users to feel free and comfort-
able when interacting with them.
2 Method
2.1 Corpus Description
For the analysis, a corpus containing 100 mono-
logues, 56 dialogues and 100 verbal intelligence
126
scores of the participants was used. The corpus was
collected at the University of Ulm, Germany. All
the participants were German native speakers of dif-
ferent genders, ages, educational levels and social
status. For the monologue collection, the partici-
pants were shown a short film and were asked to
describe it with their own words. The candidates
were not asked to follow the language style of the
film; they were asked to talk as naturally as possi-
ble in order to capture their every day conversation
styles. Each monologue is about 3 minutes long and
contains 370 words on an average. For the dialogue
collection, the participants were asked to have a 10-
minute conversation with another test person. The
topic of the discussions was always the same: the
education system in Germany. The average num-
ber of turns in the dialogues is 55. Afterwards,
verbal intelligence of the candidates was measured
using the Hamburg Wechsler Intelligence Test for
Adults (Wechsler, 1982). Using this test, we ob-
tained verbal intelligence scores of the test persons
with a mean value of 113 and a standard deviation of
7.2. A more detailed description of the corpus can
be found in (Zablotskaya et al., 2010; Zablotskaya
et al., 2012).
2.2 Clustering
Using the k-means algorithm, the verbal intelligence
scores of the test persons were partitioned into:
a) 2 clusters (Cluster L consisted of test persons
with lower verbal intelligence, H contained
candidates with higher verbal intelligence);
b) 3 clusters (L - lower verbal intelligence, M -
average verbal intelligence, H - higher verbal
intelligence).
Using the two clusters L and H, all the dialogues
were partitioned into the following groups:
c) L-L is a group of dialogues where both partners
had lower verbal intelligence scores;
d) H-H is a group of dialogues where both part-
ners had higher verbal intelligence scores;
e) L-H is a group of all the other dialogues.
Using the information about the level of acquain-
tance of the dialogue partners, the following groups
were created:
f) F-F is a group of dialogues with dialogue part-
ners who were friends or relatives;
g) S-S is a group of dialogues with dialogue part-
ners who had not met each other before the ex-
periment (were strangers).
In the following sections the degree of adaptation
will be compared between these groups.
3 Measuring Adaptation
There exist different approaches for measuring
adaptation of dialogue partners. Reitter et al. (2006)
used regression models to show that a speaker in
human-human interactions aligns his syntactic struc-
tures with those of his dialogue partner. Ward and
Litman (2007) modified the measures of conver-
gence offered by Reitter. According to this mod-
ification, prime words of the first dialogue part-
ner were determined. For measuring lexical con-
vergence, the use of prime words by the second
dialogue partner for each turn was calculated. In
(Nenkova et al., 2008) the measurements of adapta-
tion between dialogue partners were based on the us-
age of high-frequency words. Stoyanchev and Stent
(2009) analysed adaptation calculating the number
of reused verbs and prepositions by a speaker that
occurred in his dialogue partner’s turns.
In this work we measure adaptation as cross ref-
erencing, proportion of “I”, “You” and “We” words,
between-subject correlation and similarity between
two texts. These approaches are described in the fol-
lowing sections.
3.1 Cross Referencing
Cross referencing is calculated as a number of re-
peated nouns and adjectives by a speaker P1 from
his dialogue partner P2 divided by the total number
of P1’s words (Sillars et al., 1997).
A one-way analysis of variance (ANOVA)
showed significant difference between Cross refer-
encing of speakers from the groups L, M and H
(AVL = 0.08, AVM = 0.047, AVH = 0.042, F(2,97) =
8.43, p = 0.00062). As we may see, speakers with
lower verbal intelligence reused more nouns and ad-
jectives of their dialogue partners than speakers with
average and higher verbal intelligence.
127
3.2 “I”, “You” and “We” words
The number of “I”, “You” and “We” words in a dis-
cussion may reflect the degree of closeness of speak-
ers. In Sillars et al. (1997) these measures were used
for the analysis of language use in marital conver-
sations and closeness of relationships between part-
ners. It was found out that partners who had lived
with each other for a long time and were happy to-
gether used “we” pronouns more often than sepa-
rate pairs. In addition, the proportion of “I” and
“You” words were higher for separates. In our in-
vestigation we also calculated the proportion of “I”,
“You” and “We” words for each groups and com-
pared them using ANOVA. Interestingly, the pro-
portion of “I”-words of friends was greater than
that of strangers (averaged value of “I”-words for
friends AVF = 0.0033, for strangers AVS = 0.0017,
F(1,109) = 5.33, p = 0.024). This phenomena
may be explained in the following way. Even dis-
cussing the German education, friends might talk
about themselves. People who had not met each
other before avoided talking too much about their
own experience. On the other hand, the difference
of “We”-words was not significant. This means that
even friends were not able to linguistically show
their closeness discussing such kind of topic.
3.3 Between-Subject Correlation
All the dialogue transcripts were compared with the
LIWC dictionary for the German language (Wolf et
al., 2008). The dictionary consists of different words
sorted by 64 categories. The categories may be di-
vided into the following groups:
• Language composition, for example number of
words, number of unique words, pronouns, ar-
ticles, etc.
• Psychological processes, for example positive
and negative emotions, causal words, words ex-
pressing certainty, etc.
• Relativity, for example words related to space,
motion and time.
• Topic-related categories, for example job,
school, sleep, etc.
Each word from the dictionary may refer to sev-
eral categories. For example, the word traurig (sad)
refers to the categories Affective Processes, Negative
emotions and Sadness.
For analysing the degree of adaptation of dialogue
participants, Pearson’s correlation coefficients be-
tween F(Ai) and F(Bi) for each feature F were cal-
culated (F(Ai) is the value of a feature F extracted
from the utterances of the first dialogue partner A
from a dialogue i, F(Bi) is the value of a feature F
extracted from the utterances of the second dialogue
partner B from a dialogue i). For participants from
the group L-L, 30% of the features showed a signif-
icant correlation, for participants from the group H-
L this value was 23%, for H-H this value was 12%.
Table 1 shows the percentage of features with sig-
nificant correlation for each LIWC group.
LIWC group H-H L-L H-L
Language composition 28% 37% 9%
Psychological precesses 10% 19% 23%
Relativity 10% 35% 30%
Topic-related categories 11% 37% 27%
Table 1: Percentage of LIWC categories with significant
correlation coefficients.
As we can see from the results, for almost all
LIWC groups lower verbal intelligence speakers en-
gaged in a conversation showed a higher degree of
adaptation.
3.4 Similarity between two Texts
If two dialogue partners adapt to each other during
a conversation, the similarity between their utter-
ances should be high. For measuring the similar-
ity between two texts, we calculated the degree of
alignment between frequency distributions of cer-
tain features (tokens) extracted from the dialogues.
For comparing the frequency distributions, the chi-
square test was chosen because it does not require
the normality of distributions and is easy to imple-
ment. A detailed explanation of this method may
be found in (Vogel and Lynch, 2007) and (Straker,
2012).
Let Fi and Fj be two text files containing ni and n j
tokens correspondingly. If Fi and Fj have the same
language style, we consider the texts to be taken
from the same population and the distributions of
tokens from the two files should not be significantly
different (null hypothesis). The chi-square statistic
128
is calculated based on the observed and expected
values of tokens in both text-files. If the chi-value
χ2i is less than a certain significance threshold c2i
(based on the degrees of freedom and significance
level), the null hypothesis is accepted and the two
files may be considered as having a similar language
style (making an assumption that the language style
is reflected by tokens of this type). For estimating
the degree to which the two texts are similar, we cal-
culate the distance between these two values:
Similarityi = Si = χ2i − c2i .
If −c2i <= Si <= 0, the similarity between the texts
is significant. If Si > 0, the null hypothesis is re-
jected: the analysed texts have different language
styles.
In this investigation four different types of tokens
were extracted: Letter n-gram distributions,Word n-
gram distributions, Lemma n-gram distributions and
Part-of-speech n-gram distributions.
The mean values of Si for each group were com-
pared to each other using ANOVA. Features with
significant ANOVA results for the groups F-F and
S-S are shown in Table 2:
Feature Si for F-F Si for S-S F(1,54)
Word 3-g -48.7 -29.8 10.6
Lemma 3-g -41.8 -23.5 10.1
P.-of-speech 4-g -38.9 10.0 8.1
P.-of-speech 5-g -59.4 -34.3 8.6
Table 2: Significant features for F-F and S-S (p< 0.05).
The results show that the similarities of language
between friends or relatives were greater than be-
tween participants who had not met each other be-
fore.
Our next purpose was to check whether verbal
intelligence plays a certain role if we analyse di-
alogues between friends and strangers separately.
ANOVA was applied to the mean values of the
similarity measure Si calculated for the following
groups:
a) L-L, H-H and L-H only for dialogues between
friends;
b) L-L, H-H and L-H only for dialogues between
strangers.
ANOVA significant feature are shown in Tables 3
and 4.
Feature Si(L-L) Si(H-H) Si(L-H) F
Word 4-g -77.8 -62.4 -53.81 3.9
P.-of-sp. 6-g -83.5 -63.7 -53.9 4.7
Table 3: Significant features for L-L, H-H and L-H only
for dialogues between friends (p< 0.05, F(1,53)).
Feature Si(L-L) Si(H-H) Si(L-H) F
Word 4-g. -59.9 -90.1 -45.2 2.2
Table 4: Significant features for L-L, H-H and L-H only
for dialogues between strangers (p< 0.05, F(1,53)).
As we may see from the results, a lower verbal
intelligence speaker may adapt to his dialogue part-
ner if they both are relatives or friends. On the other
hand, if dialogue partners have not met each other
before, higher verbal intelligence speakers are bet-
ter able to adapt to their dialogue partner than lower
verbal intelligence speakers.
4 Discussions
As we may see from the results, it was difficult for
the candidates to linguistically show their closeness
discussing the education system in Germany. How-
ever, similarity of utterances in dialogues between
friends was greater than similarity in dialogues be-
tween strangers. Lower verbal intelligence speakers
repeated nouns and adjectives from their dialogue
partners and used words from the same linguistic
dimensions more often than higher verbal intelli-
gence speakers. The first hypothesis is just partly
proven because we did not find features that reflect
adaptation of higher verbal intelligence speakers. In
our future work we are going to further investigate
how higher verbal intelligence speakers linguisti-
cally show their closeness to the other. The results
also showed that speakers with lower verbal intelli-
gence are better able to adapt to the other if they both
are relatives or friends. As we suggested in our sec-
ond hypothesis, if dialogue partners are strangers,
higher verbal intelligence speakers show a higher
degree of adaptation. In our future work we are go-
ing to use this information for improving the clas-
sification of speakers into two and three groups ac-
cording to their verbal intelligence coefficients.
129
Acknowledgments
This work is partly supported by the DAAD (Ger-
man Academic Exchange Service).
Parts of the research described in this arti-
cle are supported by the Transregional Collabora-
tive Research Centre SFB/TRR 62 “Companion-
Technology for Cognitive Technical Systems”
funded by the German Research Foundation (DFG).
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Estimating Adaptacion of Dialogue Partners with Different Verbal Intelligence

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