Timing Prediction of Facilitating Utterance inMulti-party Conversation
Tomonobu Sembokuya1 and Kazutaka Shimada1
Department of Artificial Intelligence, Kyushu Institute of Technology
680-4 Iizuka Fukuoka 820-8502 Japan
shimada@pluto.ai.kyutech.ac.jp

Abstract. Supporting consensus-building in multi-party conversations
is a very important task in intelligent systems. To conduct smooth, active, and productive discussions, we need a facilitator who controls a
discussion appropriately. However, it is impractical to assign a good facilitator to each group in the discussion environment. The goal of ourstudy is to develop a digital facilitator system that supports high-quality
discussions. One role of the digital facilitator is to generate facilitatingutterances in the discussions. To realize the system, we need to predict
the timing of facilitating utterances. To apply a machine learning technique to our model, we construct a data set from the AMI corpus, first.
For the construction, we use some rules based on the annotation of the
corpus. Then, we generate a prediction model with verbal and non-verbal
features extracted from discussions. We obtained 0.75 on the F-measure.
We compared our model with a baseline method. Our model outperformed the baseline (0.7 vs. 0.5 on the AUC value). The experimental
results show the eﬀectiveness of our model.
Keywords: Multi-party conversation · Timing Prediction · Facilitation.

1

Introduction

In collaborative work, people need to discuss several topics for decision-making
on a meeting, namely multi-party conversation. It is a very important task inintelligent systems to support consensus-building in conversations with multiple participants. Participants in a discussion often struggle to identify the mostsuitable solution for a decision on a meeting agenda because there are generally
many alternatives and criteria related to making a decision. As a result, theyoften fail to make a satisfying decision. It leads to the failure of the discussion.
To conduct smooth, active and productive discussions, they need an eﬀective facilitator who controls the discussion appropriately. However, it is impractical toassign an eﬀective facilitator to each group in the discussion environment due toa lack of human resources. Although a project manager needs to appropriately
handle a discussion in business meetings, he/she might not have remarkablefacilitation skills, such as asking questions to gain additional information andasking follow-up questions to further expand participants’ understanding. Ordinary people in a group discussion might subconsciously need help from others to2

Sembokuya and Shimada.
Overhead-view camera

Discussion Map
Microphone andMulti-camera images

Roles of Discussion Map
1. Management of personal
opinions and information
sharing
2. Feedback for consensusbuilding

Fig. 1. Overview of our digital facilitator system with discussion maps. The goal of ourstudy is to develop a system that behaves like a facilitator by using several modalities,
such as speech and image inputs.

generate a good decision. Therefore, a system that supports consensus-building
plays a very important role in discussion.
The goal of our study is to construct a system that cooperatively supports
consensus-building and management of conversation for high-quality discussion.
The system is referred to as a digital facilitator: a collaborative agent for participants of discussions. Figure 1 shows the overview of our system. We aredeveloping a prototype system to support real discussions [6]. The system estimates the current state of a discussion and then generates sentences and chartsthat describe it. This is a part of our digital facilitator system. However, thegeneration timing in the current system depends on participants’ clicks on thesystem: a passive control of the system. Therefore we need facilitator’s knowledge, behavior, and patterns to realize a good digital facilitator: an active control
from the system.
In this paper, we focus on timing about intervention or facilitation by thedigital facilitator. We define an utterance by a participant that behaves like achairperson on the discussion as “Facilitating Utterance.” We propose a prediction model of the timing of such utterances by using a machine learningtechnique. The contributions of this paper are as follows:Timing Prediction of Facilitating Utterance in Multi-party Conversation

3– We design a guideline for constructing training data from the AMI corpus
for a timing prediction task of facilitating utterances. It is based on dialogue
act tags and social role tags in the corpus.
– We propose a timing prediction model using verbal and non-verbal features
for facilitating utterances. We compare the eﬀectiveness of the features experimentally.

2

Related Work

Shiota et al. [14] have reported an analysis of characteristics of facilitators in
two multi-party conversations corpora: the AMI corpus [3] and the Kyutech
corpus [19]. In the analysis, they generated decision tree models to classify eachparticipant into a facilitator and a non-facilitator in the corpora. Omoto et al.
[11] have reported the analysis of facilitating behavior of the exemplary facilitatorfrom measured non-verbal and para-linguistic data. They defined four actions
for facilitation: convergence, divergence, conflict, and concretization, and thenanalyzed the conversations on the basis of these factors. However, these studies
analyzed conversations from a macro perspective. We need to determine thetiming of facilitation as a function of our digital facilitator system, namely amicro perspective.
Lala et al. [7] have proposed an approach to attentive listening, which integrates continuous backchannels with responsive dialogue to user statements tomaintain the flow of conversation in spoken dialogue tasks. They constructeda prediction model based on a logistic regression approach. The task is that abackchannel would occur in 500ms or not. In addition, they improved their system by incorporating a statement response model on the four diﬀerent responsetypes and a flexible turn-taking model. They evaluated the system with theautonomous android, Erica, as a pilot study. Skantze [16] has proposed a turntaking model using LSTM for spoken dialogue systems. The model predicted
the speech activity for an upcoming fixed time window. They also evaluated
how the hidden layer in the network can be used as a feature vector for turntaking decisions in human-robot interaction data. The target of these studies is adialogue with two persons. On the other hand, our task is to predict facilitatingutterances in multi-party conversations and discussions.

3

Data Construction

We need a data set for a prediction model based on machine learning. For the
purpose, we utilize the AMI corpus and the tag sets.
3.1

AMI Meeting Corpus

The AMI corpus [3] is one of the most famous meeting corpora. It consists of
scenario and non-scenario meetings. In this paper, we handle scenario meetings.

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Sembokuya and Shimada.

In the scenario task, participants pretended members in a virtual company,
which designs remote controls. Each participant played each role: project manager (PM), industrial designer (ID), user-interface designer (UI), and marketing
expert (ME).
The AMI corpus contains numerous annotations, such as topic tags and dialogue acts. In this paper, we focus on the dialogue act tags. The dialogue acts
denote speakers’ intentions, such as “inform” and “backchannel.” The numberof dialogue act tags is 15.
Some researchers annotated social role tags for 59 meetings on the scenario
portion of the AMI corpus [12, 17]. Each meeting was segmented into short clips
by long pauses: pauses longer than 1 second. One social role was assigned toeach speaker in each segment by annotators. Each annotator for the tagging was
asked to watch the entire video segment and assign a speaker to a role on thebasis of a list of specified guidelines. The number of social role tags is five, andthe roles are summarized as follows:– Protagonist: a speaker that takes the floor, drives the conversation, asserts
its authority, and assumes a personal perspective.
– Supporter: a speaker that shows a cooperative attitude demonstrating attention and acceptance as well as providing technical and relational support.
– Neutral: a speaker that passively accepts ideas from the others without expressing his/her ideas.
– Gatekeeper: a speaker that acts as a group moderator. He/she mediates andencourages the communication.
– Attacker: a speaker who deflates the status of others, expresses disapproval,
and attacks other speakers.
In this paper, we handle the 59 meetings with the social roles.
3.2

Facilitating Utterance

Our task is to predict the timing of facilitating utterances in conversations.
Therefore, we need to determine which utterances correspond to facilitating utterances in the conversations to apply a machine learning method for the prediction. For the purpose, we configure tree-based rules to determine the facilitatingutterances. Figure 2 shows the flowchart of the determination process.
First, we check whether each utterance is spoken by a participant with the
Gatekeeper tag. If so, move to the next step. If not so, we regard the utterance asa Non-facilitating utterance. From the definition in Section 3.1, the Gatekeeper
tag is the most important factor for the judgement of facilitating utterances.
Although project managers (PM) often have a similar role with Gatekeeper in
discussions, we focus on the Gatekeeper tags only. The reason is that participants
with other roles (ID, UI, and ME) often behave like a facilitator.
Next, we focus on specific dialogue act tags. Table 1 shows examples of the
dialogue act tags in the AMI corpus. We use “Inform”, “Suggest”, “‘Oﬀer”, and“Elicit-*” for the determination. We can correctly remove utterances with thesetags as backchannel utterances of Gatekeepers, e.g., “Uh, I see.”Timing Prediction of Facilitating Utterance in Multi-party Conversation

5

Utterance by Gatekeeper
YES

NO

Utterance contains specific DA
YES

YES

Utterance consists of
5 words or more

Facilitation utterance

Non-Facilitation utteranceNO
Non-Facilitation utteranceNO

Non-Facilitation utterance

Fig. 2. Flowchart for the determination process. The data set is constructed by usingsocial role tags and dialogue act tags in the AMI corpus.
Table 1. Dialogue acts on AMI meeting corpus.
DA tag Meaning
Backchannel Response such as “yeah”
Stall
Filled pauses
Fragment Utterance that does not convey a speaker intention
InformGiving information
Suggest Expressing an intention relating to the actions ofanother individual, the group as a whole, or a groupin the wider environment
Oﬀer
Expressing an intention relating to his or her own
actions
AssessComment that expresses an evaluation
Elicit-* Requests about the DA; e.g., if * is “Inform”,
it denotes a request that someone else give some
information.

Finally, we select utterances with five words or more as facilitating utterances.
By using this rule, we can remove utterances for giving information from thefacilitating utterance list, e.g., the utterance “No.” with the Inform tag. The
threshold, five or more, was determined experimentally1 .

4MethodWe explain our method and the task in this section. Figure 3 shows the overview
of our method. The 1st and 14th utterances with the orange color in the figure
are instances of facilitating utterances by the rules in Section 3.2.
We regard utterances within Sp sec. from the current utterance as one cluster. Then, we assign the “+1” label to the cluster that contains a facilitating
1

Five is the mode value of utterances with the Gatekeeper and specific DA tags.

6

Sembokuya and Shimada.

Slice ID

Social role

UtterancePM

Gatekeeper

Um, is there anything about the color?

A

ID

Protagonist

I suppose to prepare two colors.

D

ME

Supporter

What color?

A

ID

Protagonist

A color is white, standard one.

A

ID

Protagonist

Another color is silver, special one.

C

UI

Supporter

Mm-hmmD

ME

SupporterSounds good.

C

UI

Supporter

White can easily get dirty.

B

PM

Gatekeeper

Uh, I see.

A

ID

Protagonist

Right.

A

ID

Protagonist

So how about black instead.

C

UI

SupporterThat’s good.

B

PM

Gatekeeper

Is it okay to decide colors white and silver.
Okay, is there anything else at all it can do?

BPM

Gatekeeper

D

ME

Supporter

Mm okay.

A

ID

Supporter

Mm-hmm

t-4

t-3

t-2

Data

t-1

t
Current
TargetLabel

ES2002d_212_241

Scenario role

B

Features

ES2002d_179_212

SpeakerFig. 3. Overview of our timing prediction model. Our model extracts features from theprevious utterances in each Sa and concatenates the features for the prediction model.
The label denotes +1 if the Sp contains a facilitating utterance and -1 if the Sp does
not contain a facilitating utterance.

utterance, otherwise -1. Our model predicts the label by using features extracted
from utterances in the previous Sa range. We concatenate features of five Sa .
We use Support Vector Machines (SVMs) as the classifier. We utilize LIBSVM [4] for the implementation. The parameters are default settings, and thekernel is RBF. For SVMs, we extract the following features:f1) Average of word embedding
The word embedding is a vector representation of each individual word which
is pre-trained by some of the syntactic and semantic relationships in thelanguage [9]. We use a model trained from Wikipedia and Web news. We
utilize fastText [2] for the implementation. We calculate the average valueof the embedding vectors of words that appear in Sa .
∑xi ∈W ords(t,Sa ) Emb(xi )Embave (t, Sa ) =(1)Sa
where Emb(xi ) is the embedding of a word xi based on fastText. t denotes
time in the discussion and the unit of Sa is seconds.
f2) Average of words in each Sa
Participants tend to frequently utter his/her thoughts and opinions in heatedTiming Prediction of Facilitating Utterance in Multi-party Conversation

7

discussion. They also tend to not frequently utter his/her thoughts and opinions in non-hated discussion. It indicates that the number of utterances is
one important feature for the prediction. Here, we utilize the average numberof words in Sa as a feature for the prediction model.
N umW ordsrate (t, Sa ) =N umW ords(t, Sa )Sa

(2)

where N umW ords is the number of words in Sa in t.
f3) Ratios of overlap and silence
In a similar situation to f2, overlaps and silences occur in discussion. To
capture the tendencies of each participant about the two characteristics, we
introduce ratios of overlaps and silences as the features. The feature values
are also the average values of overlap length and silence length in Sa .
∑sli ∈Silents(t,Sa ) sli(3)
SilentT imeratio (t, Sa ) =Sa∑oli ∈(t,Sa ) oli
OverlapT imeratio (t, Sa ) =(4)Sa
where sli and oli are silence length and overlap length in Sa , respectively.
f4) Number of long silences
Long silences often indicate non-heated discussion as compared with shortsilences. Therefore, we detect silences that are longer than a threshold andthen utilize the frequency of the long silences as the feature2 .
N umSilentsT (t, Sa ) =∑sli ∈Silents(t,Sa ){1 (sli ≥ T )0 (sli < T )(5)where t is a threshold for a long time silence.
f5) Number of speaker changes
In heated discussion, speaker changes occur frequently. Therefore, we utilize
the number of speaker changes in Sa as the feature.

5

Experiment

We evaluated our timing prediction model with the dataset described in Section
3. We set Sp = 30 seconds because the average time of each segment in social roleannotation in the previous studies was approximately 30 seconds. We evaluatedthe dataset with 10-fold cross-validation. We analyzed our method and the datain terms of types of features, length of Sa , and types of dialogue acts. In addition,
we compared our model with a baseline.
2

For overlaps, we do not handle this feature because the overlap length is usuallyshorter as compared with the silence length in discussion.

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Sembokuya and Shimada.
Table 2. Comparison on features.
Features
Verbal
Non-Verbal
All

5.1

P
0.74
0.73
0.74

R
0.76
0.55
0.76

F
0.75
0.63
0.75

Discussion about Features

To discuss the eﬀectiveness of features, we categorize the features into two types:
verbal and non-verbal features. Here, the verbal features are f1 and f2, andthe non-verbal features are f3, f4 and f5 described in Section 4. We generated
three prediction models: a model with verbal features, a model with non-verbal
features, and a model with all features. Then, we evaluated the models with
precision (P), recall (R), and F-values. We set Sa = 30 in this experiment.
Table 2 shows the experimental result. From the result, the non-verbal features were not essentially eﬀective for the prediction. The values of the modelwith verbal features and the model with all features were the same in all criteria.
However, instances that the models predicted incorrectly were not completely
the same. Some instances were predicted correctly by using the ratio of overlaps in f3. Therefore, non-verbal features are not always counterproductive tothe prediction. We need to discuss more eﬀective nonverbal features throughdetailed error analysis.
To achieve better accuracy, we need to apply other features that are obtained
from speech information for the prediction model. Prosodic features, such as
pitch and volume, were often used in studies about participant’s role recognition [13, 18]. As a verbal feature, we utilized the average vector based on wordembedding. However, the average vector lost some information, such as fillers
in utterances. We need to discuss the eﬀectiveness of some specific words andphrases for the improvement of the model.
5.2

Discussion about Sa

In Section 5.1, we set Sa = 30. However, it is not clear which Sa is appropriate
for the prediction model. Therefore, we compared diﬀerent settings about Sa(Sa = 5, 10, 20, and 30.) We used all features in the comparison because thesetting was the best in Section 5.1.
Table 3 shows the experimental result. We obtained better results for Sa =20
and 30, as compared with the smaller values of Sa .
We analyzed the diﬀerence in the results between Sa = 5 and Sa = 30 indetail. First, we discuss the case that the setting Sa = 30 was better than thatof Sa = 5. For the case that Sa = 30 was better, a long silence often appeared in
the Sa . It indicates the decrease of the number of utterance in the Sa . It led tothe decrease of the number of words. Since the verbal features were eﬀective in
our model, long Sa was important to capture the features. On the other hand,Timing Prediction of Facilitating Utterance in Multi-party Conversation

9

Table 3. Comparison on Sa .
Sa [sec]
5
10
20
30

P
0.71
0.74
0.75
0.74

R
0.71
0.72
0.75
0.76

F
0.71
0.73
0.75
0.75

Table 4. Recall about each DA.
DA tagInformSuggestOﬀer
Elicit-InformElicit-Oﬀer-or-Suggestion
Elicit-Assessment
Elicit-Comment-about-Understanding

Num
1305
311
122
178
31
104
2

R
0.75
0.76
0.78
0.79
0.77
0.83
0.5

the setting Sa = 5 was unfitted and unsuitable in the case that Sa contained
a long silence because the model cannot adequately capture the verbal featuresdue to a small number of words.
Next, we discuss another situation, that is Sa = 5 was better. In this situation, the problem was also opposite to the setting that Sa = 30 was better. In
other words, Sa = 5 performed well in the case that Sa contained many words
and no long silence in Sa . Information about important words in Sa vanished by
the average vector generated from too many words. Thus, the optimal length of
Sa depends on the tendency in each Sa .
The goal of this model is to detect the timing of facilitating utterances for our
digital facilitator system. In other words, we need to ensure real-time prediction
in discussion. Thus, the small Sa is essentially suitable although the current
result is the opposite. We need to investigate the optimal Sa through the additionof eﬀective features described in Section 5.1.
5.3

Discussion about Dialogue Act

As we used Dialogue Act (DA) tags in the data construction, DA tags are closely
related to social roles because DA tags denote the intention of each utterance.
Hence, we compared the recall rate of each DA tags in the model with all features3 and Sa = 30.
Table 4 shows the experimental result about some specific DA tags that areclosely related to facilitating utterances, such as Inform and Elicit-*. “Num” inthe table denotes the number of instances with each DA tag in the experimental
3

Note that our model did not use any DA tags as features.

10

Sembokuya and Shimada.
Table 5. Comparison with a baseline.
Model
Baseline
Ours

P
0.65
0.74

R
1.00
0.76

F
0.79
0.75

AUC
0.50
0.70

data set. We almost obtained balanced results, namely 0.75 or more. Although
the recall rate of “Elicit-Comment-about-Understanding” was not enough, thereason was the number of instances in the data set.
The DA tag with the best recall rate was “Elicit-Assessment.” In other words,
the previous utterances of “Elicit-Assessment” contained much information forthe prediction. The guideline of the AMI corpus [1] said,
In an ELICIT-ASSESSMENT, the speaker attempts to elicit an assessment (or assessments) about what has been said or done so far. Sometimes a speaker seems to be making a suggestion and eliciting an assessment about it at the same time.
This definition entails a part of the roles of facilitating utterances. However,utterances with the DA tags listed in Table 4 are not always facilitating utterances. Therefore, it is insuﬃcient to predict timing of facilitating utterancesby using only the DA tags although they are important features for predictionof facilitating utterances. Moreover, DA tags cannot be applied to a real-time
prediction model easily due to need of annotation by human annotators. On theother hand, our model was able to predict facilitating utterances in a relatively
high accuracy without DA tag information.

5.4

Comparison with a Baseline

We compared our model with a baseline. The baseline was based on a simple andnaive assumption; each Sp always contains one or more facilitating utterances. In
other words, the baseline always produced +1 for each Sp , namely the majorityof the label in the data set. For the evaluation, in addition to P, R, and Fvalues, we introduce the Area Under the Curve (AUC) of the Receiver Operating
Characteristic (ROC). The ROC curve is created by plotting the true positive
rate (TPR) against the false positive rate (FPR) at various threshold settings.
The AUC is the area under the ROC curve. Therefore the AUC of the baseline
becomes 0.5 from the definition.
Table 5 shows the experimental result. The F-value of the baseline was morethan that of our model due to the perfect Recall rate. However, our method
outperformed the baseline in terms of the AUC value. It shows the eﬀectivenessand appropriateness of our method as compared with the baseline.

Timing Prediction of Facilitating Utterance in Multi-party Conversation

6

11

Conclusion

In this paper, we proposed a model for predicting the timing of facilitatingutterances for the digital facilitator. We defined an utterance by a participantthat behaves like a chairperson on the discussion as facilitating utterances. We
designed a guideline for constructing training data from the AMI corpus for atiming prediction task of facilitating utterances. It was based on dialogue act
tags and social role tags in the corpus.
We applied verbal and non-verbal features for the timing prediction modelof facilitating utterances. We evaluated our model in terms of types of features,
length of Sa , types of dialogue acts, and comparison with a baseline. The verbal
features were eﬀective for the prediction. The non-verbal features also performeda certain function for the prediction. Our model also outperformed a simple
baseline in terms of the AUC value. As a whole, the experimental results show
the eﬀectiveness of our prediction model.
To achieve better accuracy, we need to apply other features that are obtained
from speech information, such as pitch and volume. We also need to discuss theeﬀectiveness of some specific words and phrases for the improvement of themodel. In the experiment, the Sa of the best F-value was 30. However, theoptimal length of Sa depends on the tendency in each Sa . Dynamic setting of
the Sa is interesting future work.
In this paper, we handled the AMI corpus as the data set. We have also
developed a multi-party conversation corpus, the Kyutech corpus [19]. Shiota etal. [14] reported the diﬀerence between the AMI corpus and the Kyutech corpus,
for the facilitators’ behavior. Therefore, analysis and evaluation on our corpus
for this timing prediction are important future work.
One big issue of our work is the validity of the rules for determination offacilitating utterances in Section 3.2. We automatically constructed the training
data by using the rules. The rules were based on annotated tags in the AMI
corpus and our heuristics, and were intuitively plausible. However, we need to
discuss the validity more deeply through manual data analysis.
There are many approaches and studies to build mutually agreeable solutions and a consensus, such as multi-agent systems for negotiation [15] and alarge-scale online discussion [5]. Studies about facilitation robots [8] and humancommunication skills [10] are also related to our work. The knowledge from thesestudies would lead to the improvement of our work.

Acknowledgment
This work was supported by JSPS KAKENHI Grant Number 17H01840.

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