Learning Influences from Word Use in Polylogue
Tomoharu Iwata, Shinji Watanabe
NTT Communication Science Laboratories
2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan{iwata.tomoharu,watanabe.shinji}@lab.ntt.co.jp

Abstract
We propose a probabilistic model for estimating influencesamong speakers from conversation data with multiple people.
In conversations, people tend to mimic their companions’ behavior depending on their level of trust. With the proposedmodel, we assume that the word use of a speaker depends on theword use of previous speakers as well as their own earlier word
use and the general word distribution. The influences can be efficiently estimated by using the expectation maximization (EM)algorithm. Experiments on two meeting data sets in Japaneseand in English demonstrate the effectiveness of the proposed
method.
Index Terms: conversation analysis, influence, latent variable
model

1. Introduction
In conversations, people tend to mimic such aspects of theircompanions’ behavior as postures [1], facial expressions [2],lexicon [3, 4], syntax [5], and amplitude [6]. This phenomenon
is known in the literature as entrainment, accommodation, adaptation, or alignment [7]. Entrainment is said to indicate thatpeople are trusting, accommodating and empathic [1, 8].
This paper focuses on the entrainment of lexicon in polylogue, or how people are influenced by their companions interms of word use. The degree to which a person exerts an influence and is influenced by others varies from speaker to speaker.
A powerful person is likely to be mimicked by others, and apassive person might often be accommodating to others. The
influences also differ between pairs depending on their level oftrust. For example, Alice might use words spoken by Bob, but
not words spoken by Charlie. The influences therefore have an
asymmetric nature.
We propose a simple probabilistic model for estimating influences among speakers from conversation data with multiple
people. With the proposed model, we assume that a speaker’s
word use (word distribution) depends on the preceding word
use of other speakers as well as his/her own preceding word use
and the general word distribution. We estimate the strength of
influence for each pair of speakers using the expectation maximization (EM) algorithm [9].
In recent years, a huge amount of conversation data havebeen accumulated due to the improvement of recoding devices
and automatic speech recognition systems, and there has been
great interest in the analysis of conversation. For example, [4]investigated the correlation between task success and similarityof word use, and [8] analyzed the relationship between social
game results and word repetition. However, they focused on
dyadic conversations, and did not consider the asymmetric nature of influences. On the other hand, we deal with the conversation of multiple people, in which influence and sensitivenessare assumed to depend on the pair of speakers. In addition,
since the proposed method is a probabilistic generative model
for conversations, we can efficiently estimate inferences in a
principled statistical framework, and use it for a language modelof the conversation. A number of language models for conversation have been proposed [10, 11]. However, they do not aim
to estimate influences between speakers.

2. Proposed Model
Let w = {w1 , · · · , wt , · · · } be a word sequence of apolylogue, where wt represents the tth word, and let s ={s1 , · · · , st , · · · } be its speaker sequence, where st indicatesthe speaker of the tth word. Here, wt ∈ {1, · · · , W } andst ∈ {1, · · · , M }, where W is the vocabulary size, and M
is the number of participants.
In the proposed model, we assume that a speaker’s word
use depends on the preceding word use of other speakers. The
preceding word use of speaker m at position t can be modeled
as follows:t−1
PC (w|wt−τ
, m)
Pt−1

=

P

w0

t0 =t−τ δ(w, wt )δ(m, st ) + β
0

Pt−1

0

0
0
0
t0 =t−τ δ(w , wt )δ(m, st ) + βW,

(1)where τ represents the period of the influence, β is a smoothing
parameter, and δ(x, y) is Kronecker’s delta, i.e. δ(x, y) = 1 if
x = y, and 0 otherwise. This probability is proportional to thenumber of times word w is used by speaker m in the preceding
period τ . The smoothing parameter β is introduced to avoid thezero probability problem.
The word use of speaker n at position t is then modeledby a mixture of the preceding word use of the participants asfollows:t−1
P (w|wt−τ
, n)

=

M
X

t−1
λnm PC (w|wt−τ
, m)

m=1

+(1 −

X

λnm )PG (w),(2)

mwhere λnm represents the influence of speaker m on speakern, 0 ≤ λnm ≤ 1, and PG (w) is the general word distribution,
which does not depend on the preceding conversation. The general word distribution can be obtained by using other corpora.
Speaker m who influences the word distribution of speaker n is
not observed, and m is a latent variable.
This proposed model is an extension of cache models [12]for multi-speaker conversations. The cache-based language
model integrates short-term patterns of word use into the word
distribution by means of a cache component. With the proposedmodel, we build speaker-specific cache components, and set different influences among pairs of speakers. The proposed model
can be easily extended for n-gram language models by taking an-gram sequence as input instead of unigram sequence w.

3. Inference
We estimate the influences λnm based on maximum posterior(MAP) estimation. For simplicity, we rewrite the proposed
word distribution in (2) as follows:t−1, n) =
P (w|wt−τ

M
X

t−1
, m),
λnm PC (w|wt−τ

(3)

m=0
t−1
where we set PC (w|wt−τ
, m = 0) ≡ PG (w), λn0 ≡ 1 −
PM
PM
λ
,
in
which
λ
nm ≥ 0 and
m=1 nmm=0 λnm = 1. With
this notation, the logarithm of the posterior probability of paT
rameters given the conversation data {wt=1, sTt=1 }, which is to
be maximized, is calculated as follows,

L∝

T
X

log

t=1

M
X

t−1
λst m PC (wt |wt−τ, m) +MX

log P (λn |α),

n=1

m=0

(4)
where T is the current position, and the second term represents
the prior probability for parameters λn = {λnm }M
m=0 . We use
the following Dirichlet prior with hyperparameter α:log P (λn |α) =MX

α log λnm ,

(5)

m=0

because it is conjugate to multinomial parameters λn . The inference is made more robust by introducing the priors.
We can efficiently maximize the posterior (4) by using the
EM algorithm [9]. The conditional expectation of the completedata log likelihood with priors is represented as follows:
Q

=

T X
M
X

t−1
P (m|t) log λst m PC (wt |wt−τ, m)

t=1 m=0

+

M X
M
X

α log λnm ,(6)

n=1 m=0

where P (m|t) is the probability that the tth word is influenced
by speaker m. In the E-step, we compute the probability according to the Bayes rule:t−1
λst m PC (wt |wt−τ
, m)
P (m|t) = PM
.
t−1
0
0
m0 =0 λst m PC (wt |wt−τ , m )(7)

In the M-step, we obtain the next estimate
of influences λn by
P
maximizing Q w.r.t. λn subject to M
m=0 λnm = 1:
λnm = PM

m0 =0

PT

δ(n, st )P (m|t) + α
.
PTt=1
δ(n,
st )P (m0 |t) + α(M + 1)
t=1

(8)

Note that the speaker dependent preceding word distributiont−1
PC (wt |wt−τ, m) can be calculated by (1) independent of estimating parameters Λ = {λn }Mn=1 . The general word distribut−1tion PC (wt |wt−τ
, m = 0) is assumed to be given in advance.
By iterating the E-step and the M-step until convergence, we
obtain a local optimum solution for influences Λ.

Table 1: Summary of NTT and RT07 meeting data sets.
#session #speakers#utterance #vocabulary
min max min max
NTT
6
4
4
560 918
2,098
RT07
8
4
6
337 749
3,113

4. Experimental Results
We evaluated the proposed method using the following two real
meeting transcription data sets: NTT in Japanese [13] and RT07
in English [14]. Table 1 shows a summary of the NTT and
RT07 data sets, and includes the number of sessions, vocabularysize, and the minimum and maximum number of speakers andutterances for a session. With the proposed model, we used
α = 1 and β = 10−8 for the hyperparameters, and modeled
the preceding word use by using all preceding utterances in thesession, or τ = ∞. The general word distribution PG (w) islearned by using other sessions in each data set.
We estimated the influences between speakers using the
proposed model. Figure 1 shows the result. Each node represents a speaker, and the width of the arrow represents thestrength of the influence, where only influences with λnm ≥0.1 are shown. The self influence is generally strong, which indicates that the word use depends strongly on the speaker’s own
preceding word use. This is an intuitive result. There are also
many influences between speakers. Some speakers are influential, e.g. speaker 1 in Session 7 in RT07, and some speakers
are sensitive to other speakers, e.g. speaker 2 in Session 6 in
RT07. Most of the influences are asymmetric. This result indicates that it is important to model the direction of the influences.
In all the NTT sessions, speaker 4 was appointed chairperson,
and therefore, speaker 4 was influential and not sensitive. The
result obtained with the proposed model reveals the influential
and non-sensitive characteristics of speaker 4 as shown in Figure 1 (a), where there are more than three arrows from speaker
4 in five out of six sessions, and there is no arrow pointing to
speaker 4 from others in all the sessions. In this way, the proposed model can use conversation data to analyze the influences
between speakers.
For a quantitative evaluation, we compared the following
six models:• CC has a common cache that is shared by all speakers,
and a common parameter that control the influence ofthe common cache. The word distribution is describedas follows:t−1
t−1
P (w|wt−τ, n) = λPC (w|wt−τ
)+(1−λ)PG (w), (9)

where
Pt−1
0 =t−τ δ(w, wt0 ) + β
t−1,
PC (w|wt−τ ) = P Pt t−1
00w0
t0 =t−τ δ(w , wt ) + βW
(10)
is the common cache. This is the same as the standard
cache language model.
• OC has the speaker’s own caches, and a common parameter that controls the influence of the speaker’s own
cache. The word distribution is as follows:t−1
t−1
P (w|wt−τ, n) = λPC (w|wt−τ, n) + (1 − λ)PG (w).
(11)
This model assumes that the word use depends only on
the speaker’s own preceding word use.

1

2

1

2

1

2

1

2

1

2

1

2

3

4

3

4

3

4

3

4

3

4

3

4

Session 1

Session 2

Session 3

Session 4

Session 5

Session 6(a) NTT
1
1

2

1

2

1

2

3

4

3

4

3

4

2

2

3

Session 1

Session 2

Session 3

1

6

5

1

2

3

4

5
4

3

Session 6(b) RT07

4

Session 7

Session 8

Figure 1: Estimated influences by the proposed model in some sessions.
• IC has individual caches for each speaker, and a common parameter that controls the influence of the speakerdependent caches. The word distribution is as follows:t−1
P (w|wt−τ
, n)

=

M
X

t−1
λm PC (w|wt−τ
, m)

m=1

+(1 −

M
X

λm )PG (w), (12)m=1where λm represents the influence of speaker m on
all speakers including speaker m himself/herself. Thismodel assumes that the strength of the influence depends
on the speaker, but the sensitivity does not differ among
speakers.
• CI has a common cache, and individual parameters that
control the influence of the common cache for eachspeaker as follows:t−1
t−1
P (w|wt−τ, n) = λn PC (w|wt−τ
) + (1 − λn )PG (w).
(13)
This model assumes that the word use depends on all
speakers’ word use and the degree of dependence differs
among speakers.
• OI has the speaker’s own caches, and individual parameters that control the influence depending on the speakers
as follows:t−1
t−1
P (w|wt−τ, n) = λn PC (w|wt−τ
, u)+(1−λn )PG (w).
(14)
• II has individual caches for each speaker, and individual
influence parameters. This is our proposed model in (2).
The first letter of a method’s name C/O/I represents common/own/individual caches, respectively, and the second letter of the method’s name C/I represents common/individual parameters, respectively. Only the proposed model (II) takes theasymmetricity of influences into account. With all the models,
we used α = 1, β = 10−8 and τ = ∞.
In each session, we used data until the jth utterance as
training data to learn the parameters, and used words after the(j + 1)th utterance as test data. We evaluated the performanceof each model using the perplexity of held-out words:
!
PJ
PKi
j
i=j+1
k=1 log P (wik |w1 , si )
exp −
,
(15)
PJ
i=j+1 Ki

Table 2: Average perplexities for each session.
(a) NTTsession#
CC
OC
IC
CI
OI
II
1
259.2 251.4 256.5 259.4 249.9 247.7
2
279.1 263.2 280.0 278.9 264.7 261.4
297.1 287.7 298.9 298.3 288.1 284.4
3
4321.3 307.9 314.9 320.8 309.7 294.4
332.8 322.6 328.1 332.5 320.3 313.8
56
274.4 260.9 277.7 275.9 267.5 254.9
average 294.0 282.3 292.7 294.3 283.4 276.1
session#
1
2
3
4
5
6
7
8
average

CC
395.7
304.3
322.6
373.0
300.5
342.5
340.6
340.7
340.0

(b) RT07
OCIC
411.8 396.7
308.5 308.6
330.6 324.4
386.1 369.0
301.9 299.9
343.7 368.2
350.7 345.4
346.7 345.7
347.5 344.8

CI
397.1
301.6
322.9
377.2
303.2
350.4
355.8
344.9
344.1

OI
412.7
309.4
333.6
390.7
304.4
352.3
357.4
357.9
352.3

II
395.6
296.1
313.2
368.7
293.4
340.4
332.3
340.3
335.0

where J is the number of utterances in the session, Ki is thenumber of words in the ith utterance, wik is the kth word in
the ith utterance, w1j is a set of words until the jth utterances,and si is the speaker of the ith utterance. A lower perplexity
represents higher predictive performance.
Table 2 shows the average perplexities for the NTT and
RT07 data sets, in which the number of training utterances
ranges from j = 10 to j = 300. The proposed model achieved
the lowest perplexities in all sessions. This result indicates that
it is important to estimate the asymmetric influences between
speakers, which only the proposed model considers. Figure 2
shows the perplexities with different numbers of training utterances for some sessions. Generally speaking, the perplexity decreased as the number of training utterances increased
because the estimation accuracy of the influences and preceding word use improves. In some sessions, for example Session 3 in the NTT data set, the perplexity increased because of
the change of topics. Except when the number of training utterances was small, the perplexity of the proposed model (II)steadily achieved the lowest perplexities.

Session 1

Session 2
II
OI
CI
IC
OC
CC

280
270

330
II
OI
CI
IC
OC
CC

290
285
perplexity

290

perplexity

Session 3

295

260
250

280

270
265

230

260

220

310

275

240

100

150

200

250

300

300
290
280

255
50

II
OI
CI
IC
OC
CC

320

perplexity

300

270
50

100

j

150

200

250

300

50

100

j

150

200

250

300

250

300

j

(a) NTT
370
II
OI
CI
IC
OC
CC

perplexity

420

350
340
perplexity

430

360
II
OI
CI
IC
OC
CC

360

410
400

340

330
320
310

310

290

380

280
50

100

150

200

250

300

330
320

300

390

II
OI
CI
IC
OC
CC

350

perplexity

440

300
50

100

j

150

200

j

250

300

50

100

150

200

j

(b) RT07
Figure 2: Perplexities with different numbers of training utterances for some sessions. The horizontal axis represents the number oftraining utterances.

The average computational time for learning parameters in
the proposed model with 300 training utterances was 0.01 and0.02 seconds for the NTT and RT07 data sets, respectively. The
proposed model is very efficient, and it can be used in real time
applications [13].

5. Conclusion
We have proposed a probabilistic model for learning influencesfrom conversation data with multiple speakers. We have confirmed experimentally that the proposed model can extract influences between speakers and learn conversation’s word use.
Although our results have been encouraging to date, ourmodel can be further improved in a number of ways. First, we
would like to estimate influences using other behaviors, suchas nonverbal speech acts, posture and eye movement, as well
as word use. Second, the proposed model can be extended bymodeling the dynamics of influences although in this paper weassume that the influences do not change over time. Third, wemust determine the period of the influence automatically. Finally, we would like to evaluate the proposed model in an automatic speech recognition system.

6. References[1] T. L. Chartrand and J. A. Bargh, “The chameleon effect: theperception-behavior link and social interaction,” Journal of Personality and Social Psychology, vol. 76, no. 6, pp. 893–910, 1999.
[2] U. Dimberg, “Facial reactions to facial expressions,” Psychophysiology, vol. 19, no. 6, pp. 643–647, 1982.
[3] S. Brennan, “Lexical entrainment in spontaneous dialog,” in ISSD
’96, 1996, pp. 41–44.
[4] A. Nenkova, A. Gravano, and J. Hirschberg, “High frequency

word entrainment in spoken dialogue,” in ACL ’08: HLT, 2008,
pp. 169–172.
[5] D. Reitter, F. Keller, and J. D. Moore, “Computational modellingof structural priming in dialogue,” in NAACL ’06, 2006, pp. 121–
124.
[6] R. Coulston, S. Oviatt, and C. Darves, “Amplitude convergencein children’s conversational speech with animated personas,” in
ICSLP ’02, vol. 4, 2002, pp. 2689–2692.
[7] H. Giles, J. Coupland, and N. Coupland, Accommodation theory:
Communication, context, and consequence. Cambridge University Press, 1991.
[8] L. E. Scissors, A. J. Gill, and D. Gergle, “Linguistic mimicry andtrust in text-based cmc,” in CSCW ’08, 2008, pp. 277–280.
[9] A. Dempster, N. Laird, and D. Rubin, “Maximum likelihood fromincomplete data via the EM algorithm,” Journal of the Royal Statistical Society, Series B, vol. 39, no. 1, pp. 1–38, 1977.
[10] G. Ji and J. Bilmes, “Multi-speaker language modeling,” in HLTNAACL ’04, 2004, pp. 133–136.
[11] M. Purver, T. L. Griffiths, K. P. Körding, and J. B. Tenenbaum,“Unsupervised topic modelling for multi-party spoken discourse,”
in ACL ’06, 2006, pp. 17–24.
[12] R. Kuhn and R. D. Mori, “A cache-based natural language model
for speech recognition,” IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol. 12, no. 6, pp. 570–583, 1990.
[13] T. Hori, S. Araki, T. Yoshioka, M. Fujimoto, S. Watanabe, T. Oba,
A. Ogawa, K. Otsuka, D. Mikami, K. Kinoshita, T. Nakatani,
A. Nakamura, and J. Yamato, “Real-time meeting recognition andunderstanding using distant microphones and omni-directional
camera,” in SLT ’10, 2010, pp. 412–417.
[14] J. G. Fiscus, J. Ajot, and J. S. Garofolo, “The rich transcription
2007 meeting recognition evaluation,” in Multimodal Technologies for Perception of Humans, 2008, pp. 373–389.