Analyzing the Effect of Entrainment on Dialogue Acts
Masahiro Mizukami†, Koichiro Yoshino†, Graham Neubig†, David Traum‡, Satoshi Nakamura††Nara Institute of Science and Technology, Japan‡USC Institute for Creative Technologies, USA
masahiro-mi@is.naist.jp

Abstract

If the answer to these questions is affirmative,
it will be necessary to model entrainment not only
on the lexical level, but also on the higher levelof dialog flow. In addition, it will be necessary
to adapt any entrainment features of dialogue systems to be sensitive to dialogue acts or dialogue
progression. Modeling such entrainment phenomena appropriately has the potential to increase thenaturalness of the conversation and open new avenues in human-machine interaction.
In this paper, we perform a study of entrainment in an attempt to answer these three questions. First, we observe the entrainment of dialogue acts, measuring whether the choice of dialogue acts synchronizes with that of the dialogue
partner. For example, if one dialogue participant
tends to ask questions frequently, we may hypothesize that the number of questions from the partner may also increase. Secondly, we examine lexical entrainment features given dialogue acts. It is
known that dialogue acts strongly influence content of utterances, and we hypothesize that, in thesame manner, dialogue acts may strongly influence the level of lexical entrainment. Finally, we
examine the increase of entrainment as dialogue
progresses. Previous work has discussed that entrainment can be observed throughout the whole
dialogue, but it is unclear whether entrainment increases in latter parts of the dialogue. To measure
this, we divide dialogues in half, and compare theentrainment of the former and latter halves.
Experimental results show that entrainment of
dialogue acts does occur, indicating that it is necessary for models of dialogue to consider this fact.
In addition, we find that the level of lexicon synchronization depends on dialogue acts. Finally,
we confirm a tendency of entrainment increasing
through the dialogue, indicating that dialogue systems may need to progressively adapt their models
to the user as dialogue progresses.

Entrainment is a factor in dialogue that
affects not only human-human but also
human-machine interaction. While entrainment on the lexical level is well documented, less is known about how entrainment affects dialogue on a more abstract,
structural level. In this paper, we investigate the effect of entrainment on dialogue
acts and on lexical choice given dialogue
acts, as well as how entrainment changes
during a dialogue. We also define a novel
measure of entrainment to measure thesevarious types of entrainment. These results may serve as guidelines for dialoguesystems that would like to entrain withusers in a similar manner.

1

Introduction

Entrainment is a conversational phenomenon inwhich dialogue participants synchronize to eachother with regards to various factors: lexical
choice (Brennan and Clark, 1996), syntax (Reitterand Moore, 2007; Ward and Litman, 2007), style(Niederhoffer and Pennebaker, 2002; DanescuNiculescu-Mizil et al., 2011), acoustic prosody(Natale, 1975; Coulston et al., 2002; Ward andLitman, 2007; Kawahara et al., 2015), pronunciation (Pardo, 2006) and turn taking (Campbell andScherer, 2010; Beňuš et al., 2014). Previous works
have reported that entrainment is correlated with
dialogue success, naturalness and engagement.
However, there is much that is still unclear withregards to how entrainment affects the overall flowof the dialogue. For example, can entrainment also
be observed in choice of dialog acts? Is entrainment on the lexical level more prevalent for utterances of particular dialogue acts? Does the levelof entrainment increase as dialogue progresses?

310
Proceedings of the SIGDIAL 2016 Conference, pages 310–318,
Los Angeles, USA, 13-15 September 2016. c 2016 Association for Computational Linguistics

2

Related Works

In detail, we can express this formula with word
count CS1 (w) and CS2 (w), and all of words W as,

2.1 Varieties of entrainment

En(V ) =As mentioned in the introduction, entrainment has
been shown to occur at almost every level of human communication (Levitan, 2013), including
both human-human and human-system conversation.
In human-human conversation, Kawahara et al.
(2015) showed the synchrony of backchannels tothe preceding utterances in attentive listening, andthey investigated the relationship between morphological patterns of backchannels and the syntactic complexities of preceding utterances. Levitan et al. (2015) showed the entrainment of latency
in turn taking.
In human-system conversation, Campbell andScherer (2010) tried to predict user’s turn taking
behavior by considering entrainment. Fandriantoand Eskenazi (2012) modeled a dialogue strategy
to increase the accuracy of speech recognition byusing entrainment intentionally. Levitan (2013)
unified these two works.
One of the most important questions about entrainment with respect to dialogue systems is its
association with dialogue quality. Nenkova et al.
(2008) proposed a score to evaluate the lexical entrainment in highly frequent words, and found thatthe score has high correlation with task successand engagement. This indicates that lexical entrainment has an important role in dialogue. In
addition, it suggests that entrainment of lexical
choice is probably affected by more detailed dialogue information, such as dialogue act.

−

∑
w∈V

CS2 (w)
CS1 (w)−∑.
wi ∈W CS1 (wi )wi ∈W CS2 (wi )∑(2)Nenkova et al. (2008) used following wordclasses as V .
25MFC: 25 Most frequent words in the corpus.
The idea of using only frequent words isbased on the fact that we would like to avoid
the score being affected by the actual content
of the utterance, and focus more on the way
things are said. In addition, this filtering ofhighly frequent words removes any specificwords (i.e. named entity, speaker’s name)and words specific to the dialogue topic. This
word class was highly and significantly correlated with task success in the previous work.
We mainly used this word class in this paper.
25MFD: 25 Most frequent words in the dialogue.
This word class was correlated with task success, like 25MFC.
ACW: Affirmative cue words (Gravano et al.,2012). This word class includes alright,
gotcha, huh, mm-hm, okay, right, uh-huh,yeah, yep, yes, and yup. This class was correlated with turn-taking.
FP: Filled pauses. This word class includes uh,um, and mm. It was correlated with overlaps.

2.2 Lexical Entrainment
The entrainment score which was proposed by
Nenkova et al. (2008) is calculated by word counts
in a corpus, and comparing between dialogue participants. Specifically, we calculate a uni-gram
language model probability PS1 (w) and PS2 (w)based on the word frequencies of speakers S1 andS2 , and calculate the entrainment score of wordclass V , En(V ) as:∑
En(V ) = −|PS1 (w) − PS2 (w)| . (1)ACW and FP were pre-defined, but 25MFC and
25MFD are calculated from corpora considering
frequency (V is a subset of W ).
In order to use these measures to confirm
whether entrainment is occurring between dialogue partners, these scores can be compared between the actual conversation partner, and an arbitrary other speaker from the database. If entrainment is actually occurring, then the score will behigher for the conversation partner than the score
for the non-partner. Figure 1 shows an example ofpairs used for calculation of these scores.
First, to confirm the results for previous work,
we calculated the entrainment score of 25MFC using the Switchboard Corpus (Table 1). We can seethat there is a difference of the entrainment scorew∈V

These entrainment scores have a range from -2
to 0, where higher means stronger entrainment.
We calculate the average of these entrainmentscores for the dialogue partner (Enp (V )) and nonpartners (Ennp (V )).

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ing procedure:
1. Calculate the entrainment score of the dialogue partner Enp (V ). Also calculate entrainment scores of all non-partners in the
corpus Ennp1,...,N (V ).
2. Compare the partner’s entrainment score andall non-partners’ entrainment scores.
Win(Enp (V ), Ennpi (V ))(Enp (V ) > Ennpi (V ))1= 0.5 (Enp (V ) = Ennpi (V ))
0(Enp (V ) < Ennpi (V ))Figure 1: How to compare scores between thepartner and non-partners
Table 1: The entrainment score of 25MFC
Partner Non-Partner
En(25MFC) -0.211-0.248

3. Calculate the ratio with which the partner’s
entrainment score exceeds that of the nonpartners.
Ratio(V)
1 ∑= |N | i∈N Win(Enp (V ), Ennpi (V ))between “partner” who is talking the speaker and“non-partner” who is not talking with the speaker,
as reported in previous work.

3

Extending the Entrainment Score

Because this score is the ratio that dialogue
with the partner takes a higher entrainment scorethan other combinations with non-partners, it is
not sensitive to the actual value of the entrainment score, but only the relative value compared
to non-partners. This makes it more feasible tocompare between phenomena with different vocabulary sizes, such as lexical choice and dialogue
act choice. While the entrainment score for dialogue acts may be systematically higher due toits smaller vocabulary size, the relative score compared to non-partners can be expected to be approximately equal if the effect of entrainment is
the same between the two classes.

Our first contribution is an extension to the entrainment score that allows us to more accurately
clarify the hypotheses that we stated in the introduction. This is necessary because the entrainmentscore given in Eqn. (2) does not consider the total
size and variance of data to be calculated, and canbe heavily influenced by data sparsity. This resultin the score being biased when we compare target
phenomena with different vocabulary sizes or data
sizes.
For example, when considering the amount ofentrainment that occurred for two different speakers, the entrainment score will tend to be higher
for the more verbose speaker, regardless of theamount of entrainment that actually occurred. In
addition, if we are comparing entrainment for two
different sets of target phenomena, such as words
and dialogue acts, the entrainment score will tend
to be higher for the phenomenon that has a smaller
vocabulary and thus less sparsity (in this case, dialogue acts). Thus, we propose a new “Entrainment Score Ratio” measurement that uses the rank
in entrainment score, and language model smoothing to alleviate the effects of sparsity.

3.2 Dirichlet Smoothing of Language Models
While the previous ratio score has the potential
to alleviate problems due to comparing different
types of phenomena, it does not help with problems caused by comparing data sets with different
numbers of data points. The reason for this is thatthe traditional entrainment score (Nenkova et al.,2008) used uni-gram probabilities, the accuracy ofwhich is dependent on the amount of data used to
calculate the probabilities. Thus for smaller data
sets, these probabilities are not well trained, andshow a lower similarity when compared with thoseof other speakers in the corpus. In order to create
a method more robust to these size differences, we
introduce a method that smooths these probabilities to reduce differences between distributions ofdifferent data sizes.

3.1 Entrainment Score Ratio
First, instead of using the entrainment score itself,
we opt to use the relative position of the entrainment score of the partner compared to other nonpartner speakers in the corpus. The entrainmentscore ratio is calculated according to the follow-

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4 Measured Entrainment Scores

Specifically, the definition of a unigram distribution of a portion of the corpus (split by speakers, dialogue act d, part of dialogue p) using maximum likelihood estimation is,Cs (wd,p ).
wd,p ∈Wd,p Cs (wd,p )PML,s (w|d, p) = ∑In this section, we explain in detail the there varieties of entrainment that we examined.
4.1 Entrainment Score of Dialogue Acts

(3)

While entrainment of various phenomena has beenreported in previous work, it is still not clear how
entrainment affects the dialogue acts used by theconversation participants. The first thing we examine in this paper is the amount of entrainment
occurring in dialogue acts, and the entrainmentscore of dialogue acts En(D) is calculated according to the differences in distributions of dialogue
acts between dialogue participants. Frequencyof each dialogue act PDS,S1 (d) and PDS,S2 (d) of
each speaker S1 , S2 for a certain dialogue act d isused in the following equation:

When the size of data for speaker s is small, there
will not be enough data to properly estimate thisprobability. To cope with this problem, we additively smooth the probabilities by introducing
a smoothing factor α and large background language model PML (w) which was trained using allof the available data:Cs (wd,p ) + αPML (w).
wi,d,p ∈Wd ,p Cs (wi,d,p ) + α(4)This additive smoothing is equivalent to introducing a Dirichlet distribution conditioned on
PML (w) as a prior probability for the small
language model distribution of PDS,s (w|d, p)(MacKay and Peto, 1995). We choose Dirichlet smoothing because it is a simple but effective
smoothing method. We determine the hyperparameter α by defining a Dirichlet process (Teh etal., 2012) prior, and maximizing the likelihood using Newton’s method1 .
To verify that this method is effective, we calculated averages and variances of the standard
entrainment score and the entrainment score using this proposed smoothing technique (Table 2).
From the results, we can see that the entrainment score rate for partners is slightly higher
with smoothing, demonstrating that the smoothedscores are as effective, or slightly more effectivein identifying the actual conversational partner.
In addition, the difference between variances ofentrainment scores has decreased, showing thatsmoothing has reduced the amount of fluctuationin scores. This indicates that the smoothing works
effectively to reduce the negative influence of population size when we compare distributions thathave different population sizes. Because of this,for the analysis in the rest of the paper we use thissmoothed entrainment score.
PDS,s (w|d, p) = ∑En(D) = −∑d∈D

|PDS,S1 (d) − PDS,S2 (d)| . (5)4.2 Lexical Entrainment Given Dialogue Acts
In the previous work, it is reported that there isan entrainment of lexical selection between dialogue participants. However, we can also hypothesize that such entrainment is more prominent forutterances with a particular dialogue act. For example, if one dialogue participant tends to say a
specific backchannel frequently, the partner may
change to use the same backchannel. On the other
hand, when one dialogue participant has his/her
own answer for a question, he/she will likely notborrow the words from the partner.
In order to examine this effect, we extended theentrainment score for lexical selection to evaluate an entrainment of lexical selection given the
dialogue act of the utterance. The extended entrainment score En(c|d), the score for a lexical selection given a dialogue act, is defined byusing conditional language model probabilities
PDS,S1 (w|d) and PDS,S2 (w|d) of each speaker S1and S2 . Specifically, we define it as follows:
En(V |d) = −∑w∈V|PDS,S1 (w|d) − PDS,S2 (w|d)| .

(6)
Using this measure, we clarify whether entrainment of lexicons has been affected by dialogue
acts, and also which dialogue acts are more likely
to be conducive to entrainment.

1
The scripts for this and other calculations will be public
at the link below:
https://github.com/masahiro-mi/
entrainment

313

Table 2: The entrainment score variance with/without smoothing
Partnerw/o smoothing
w/ smoothing

Ratio(V)
0.671
0.706

Ave.
-0.211-0.0983

4.3 Increase of Entrainment throughDialogue

Non-Partner

Ave.
-0.248-0.123Var.
0.001810.000778

Table 3: The entrainment score of dialogue acts
Partner Non-Partner Ratio
DA -0.568**-0.7150.675* p < 0.10, ** p < 0.05

Nenkova et al. (2008) noted that the entrainmentscore between dialogue partners is higher thanthe entrainment score between non-partners in dialogue. While they reported the overall trendof the entrainment score throughout the dialogue,
whether the level of entrainment changes throughout the dialog is also an important question, as it
will indicate how dialogue systems must display
entrainment properties to build a closer relationship with their dialogue partners. If entrainment ischanging through a conversation, we can hypothesize that the entrainment score will be larger atthe end of dialogue than the score at the start ofdialogue.

6 Experimental Results
6.1 Entrainment of Dialogue Acts
First, we analyze the entrainment of dialogue acts
based on the method of Section 4.1. We hypothessize that we can observe the entrainment of dialogue acts like other previously observed factors.
To examine this hypothesis, we calculated the entrainment score of dialogue acts and compared between partner and non-partners. To measure thesignificance of these results, we calculated p-valueof entrainment scores between partner and nonpartner with the t-test.
Table 3 shows that there is a significant difference (p < 0.05) of entrainment score between
partner and non-partner, with partners scoring significantly higher than non-partners. This resultshows that the entrainment of dialogue acts canbe observed in human-human conversation, andsuggests that there may be a necessity to consider
entrainment of dialogue act selection in humanmachine interaction.

We analyzed the extent of change in entrainment by splitting one dialogue into earlier andlater parts. We calculated the entrainment score
between dialogue participants in earlier/later partsof dialogue, and compared these scores.

5

Var.
0.005370.00108

CorpusAs our experimental data, we used the Switchboard Dialogue Act Corpus, which is annotatedwith dialogue acts according to the DAMSL standard (Discourse Annotation and Markup System
of Labeling) (Jurafsky et al., 1997) for each utterance. The DAMSL has 42 types of dialogue act
tags, while there were 220 tags used in the original Switchboard Corpus, Jurafsky et al. (1997)
clustered the 220 tags into 42 rough-arained scaleclasses, and reported labeling accuracy of .80 according to the pairwise Kappa statistic.

6.2 Lexical Entrainment given Dialogue Acts
Next, we analyze the entrainment of lexical choicegiven the 42 types of dialogue acts based on themethod of Section 4.2. We can assume that the
dialogue act affects the entrainment of lexicons,
which indicates that entrainment scores are different depending on the type of the given dialogueact.
In addition, we calculate entrainment score rate
and Cohen’s d (Cohen, 1988) to evaluate the effectsize. Cohen’s d is standardized mean difference
between two groups, and can calculate the amountthat a particular factor effects a value while considering each group’s variance. If these groups
have a large difference, Cohen’s d will be larger,
with values less than 0.2 being considered small,This corpus consists of 302 male and 241 female speakers. The number of conversations is
1,155, and the number of utterances is 221,616.
Each speaker is tagged with properties of sex, age,and education level.

314

values around 0.5 being medium, and values larger
than 0.8 being considered large.
We show the result in Table 4, and emphasizescores that are over 0.5 in Cohen’s d, and over 0.55
in Ratio(V).
We can first notice an increase of the entrainment score is more prominent given some dialogue acts. Entrainment is particularly prevalent
for acts that have little actual informational content, such as greeting, backchannel, agree, answer,
and repeating.
In addition, we focus on why Conventional
Opening and Conventional Closing were increasedin the entrainment score. This is because that Conventional Opening and Conventional Closing contain greetings (“hi”, “hello”) or farewells (“bye”,“see you”), which show higher entrainment scoresthan other dialogue acts. It should be noted that
this phenomenon of performing a fixed response
to a particular utterance is also often called “coordination”, and distinguished from entrainment.
However, it is difficult to distinguish between entrainment and coordination definitely with our current measures, and devising measures to capture
this distinction is future work.
On other hand, dialogue acts that express one’s
opinion such as Apology, Action-directive, Negative non-no answers, as well as some questions do
not increase entrainment scores.

Figure 2: How we compare between earlier andlater parts

Figure 3: How to calculate p-values between eachpart in partner

From these results, we can see that there is a significant difference of entrainment score between
partner and non-partner in all of the parts. This
indicates that lexical entrainment can already beobserved in the earlier part of dialogue.
In addition, we calculated p-values with thetwo-sided t test for partner entrainment scores between each part. Figure 2 shows an example ofpairs used for calculation of p-values. We compare partner’s entrainment scores between early,
later, and cross, to indicate how the entrainmentscore changes in the partner through the dialogue.
In fact, we compare three combinations of partner’s entrainment scores, such as En(c|Earlier) andEn(c|Later), En(c|Earlier) and En(c|Cross), andEn(c|Later) and En(c|Cross). Table 6 shows thatp-values of entrainment scores between each partin the partner. We find that the value of the entrainment score of the later part increased slightly overthe entrainment score of the earlier part, but theincrease was not significant. These results showthat if there is a difference in entrainment between

6.3 Change in Entrainment through Dialogue
In addition, we analyzed the increase of entrainment based on the method of Section 4.3. We
calculated lexical entrainment scores of the earlier and later parts. “Earlier” is the entrainmentscore between utterances in the earlier part of dialogue, and “Later” is the entrainment score between utterances in the later part. We hypothesizethat “Later” will have a higher entrainment scorethan “Earlier,” as it is possible that dialogue participants will demonstrate more entrainment as theytalk for longer and grow more comfortable witheach other.
In addition, we calculate “Cross,” the entrainment score between the earlier and the later partsof dialogue. We calculated this because we canalso hypothesize that the effect of entrainment isdelayed, and words spoken in the earlier part ofthe conversation may appear in the later part of thepartner’s utterances. Figure 2 shows the pairs used
for the calculation. We show the result in Table 5.

315

Table 4: The entrainment score of lexicons given a dialogue act
Partner

Enp (V )25MFC | Conventional-closing-0.0391**
25MFC | Acknowledge (Backchannel)-0.201**
25MFC | Statement-non-opinion-0.0930**
25MFC | Statement-opinion-0.154**25MFC | Conventional-opening-0.0112**-0.203**25MFC | Segment (multi-utterance)
25MFC | Agree/Accept-0.279**-0.282**
25MFC | Appreciation
25MFC | Yes answers-0.320**25MFC | Non-verbal-0.104**25MFC | Abandoned or Turn-Exit, Uninterpretable -0.203**
25MFC | Hedge-0.170**25MFC | Wh-Question-0.147**25MFC | Backchannel in question form
-0.134**25MFC | No answers-0.199**-0.0644**
25MFC | Rhetorical-Questions
25MFC | Response Acknowledgement-0.207**25MFC | Repeat-phrase-0.115**25MFC | Other-0.16025MFC | Quotation-0.0817**25MFC | Collaborative Completion-0.0867**-0.223*25MFC | Yes-No-Question
25MFC | Hold before answer/agreement-0.104**25MFC | Summarize/reformulate-0.109**25MFC | Signal-non-understanding-0.0377**-0.134*25MFC | Declarative Yes-No-Question
25MFC | Other answers-0.0584*
25MFC | Maybe/Accept-part-0.0204
25MFC | Self-talk-0.0189
25MFC | Thanking-0.0180
25MFC | Reject-0.0670
25MFC | Negative non-no answers-0.0600
25MFC | Open-Question-0.0877-0.13425MFC | Affirmative non-yes answers
25MFC | Downplayer-0.0238
25MFC | Declarative Wh-Question-0.0147
25MFC | Action-directive-0.0935
25MFC | Dispreferred answers-0.051425MFC | Apology-0.0183
25MFC | 3rd-party-talk-0.00969
25MFC | Offers, Options Commits-0.0204
25MFC | Or-Clause-0.0502
N(Number of target speaker) = 2310, * p < 0.10, ** p < 0.05

316

Non-Partner

Ennp (V )-0.185-0.252-0.113-0.192-0.0370-0.232-0.325-0.331-0.375-0.124-0.228-0.191-0.160-0.152-0.220-0.0754-0.227-0.128-0.150**-0.0905-0.0929-0.227-0.112-0.114-0.0404-0.138-0.0620-0.0221-0.0205-0.0195-0.0696-0.0581-0.0894-0.136-0.0247-0.0152-0.0944-0.0522-0.0179-0.00955-0.0205-0.0502

Cohen’s d

1.50
0.527
0.434
0.418
0.406
0.382
0.367
0.322
0.274
0.259
0.244
0.132
0.122
0.118
0.118
0.102
0.100
0.09620.07720.07490.06160.04900.04880.03800.03770.03480.03130.02470.02350.02270.02090.01810.01660.01610.01110.007970.007480.007160.00667
0.00369
0.002220.000816

Ratio(V)
0.703
0.659
0.672
0.634
0.542
0.618
0.592
0.564
0.555
0.557
0.592
0.532
0.530
0.528
0.523
0.522
0.521
0.522
0.476
0.517
0.514
0.512
0.511
0.512
0.507
0.512
0.507
0.503
0.503
0.502
0.504
0.497
0.504
0.504
0.501
0.501
0.502
0.502
0.500
0.500
0.500
0.500

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Table 5: The entrainment score for combinationsof partPartner
Non-Partner
Rate
En(25MFC|Earlier) -0.106** -0.126
0.658
En(25MFC|Cross)-0.106** -0.127
0.666
En(25MFC|Later)-0.104** -0.126
0.674* p < 0.10, ** p < 0.05

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In this paper, we focused on the entrainment withrespect to dialogue acts and dialogue progression,and analyzed for three phenomena: the entrainment of dialogue acts, the entrainment of lexical
choice given dialogue acts, and the change in entrainment as dialogue progresses.
From the results, we found that the entrainment of dialogue acts was observed in conversation. Within dialogue systems, this has the potential to contribute to modelling of dialogue strategy,
and potentially allow the system to have a closer
relationship with the partner.
We also found that lexical entrainment has a different tendency depending on the dialogue act of
the utterance. This has the potential to contributeto models of language generation, which can consider entrainment of each dialogue act.
Finally, we analyzed the differences of entrainment depending on the part of the dialogue. From
results, we found that there is either only a slighteffect, or no effect of the part of the dialogue underconsideration.
In future works, we will try an analysis of theentrainment in dialogue that considers the effectof coordination.

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This work is supported by JST CREST.

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