INTERSPEECH 2019
September 15–19, 2019, Graz, Austria

An Incremental Turn-Taking Model For Task-Oriented Dialog Systems
Andrei C. Coman1 , Koichiro Yoshino2 , Yukitoshi Murase2 , Satoshi Nakamura2 , Giuseppe Riccardi1
1

Signals and Interactive Systems Lab, Department of Information Engineering and Computer
Science, University of Trento, Italy2
Augmented Human Communication Laboratory, Graduate School of Information Science, Nara
Institute of Science and Technology, Japan
andreicatalin.coman@studenti.unitn.it, giuseppe.riccardi@unitn.it{koichiro, s-nakamura, y-murase}@is.naist.jp

Abstract

taking point, but also as a decision point of where to start the
post-processing phase, which could include dialog management
and response generation.
Each interlocutor who takes part in a dialog ”maintains” aso-called internal state of the dialog. This state is enriched withnew information or updated during the evolution of the conversation itself. In the case of a human-machine conversation,
the machine must be able to maintain a description of the human counterpart’s intentions, including the grounding in the domain semantics. Consequently, an effective dialog system must
be equipped with a tracker able to accumulate evidence overthe sequence of utterances in the dialog and update the dialog
state according to the observations. This state directly influences the behavior of the machine and its capability of identifying the point of maximal understanding of an ongoing user
utterance. In an incremental scenario, the system can conductpost-processing and provide related responses as soon as it receives new information from the human counterpart. However,there is a trade-off between how early the turn is taken and the
dialog state tracker accuracy. The latter will be reduced if theremaining part of the user utterance turns out to be informative.
We thus define a new problem which consists of predicting thebalance point, that trades off the accuracy reduction and the incremental processing.
We build our incremental Dialog State Tracker (iDST) bytaking as reference the LecTrack model [9]. The iDST was used
as a starting block for the implementation of our incremental
Turn-Taking Decider (iTTD), which in turn is responsible for
identifying the best point that balances the accuracy of the iDST
and the early turn-taking moment using the least amount of tokens possible. We exploit the dialog corpus annotated with dialog state released within the Dialog State Tracking Challenge
2 (DSTC2) [13] as our dialog domain. This dataset only provides the dialog state at the end of a turn and not for each token,
highlighting the need for more granular feedback. To meet thisneed, we use the iDST to identify the balance points within theuser utterances. Those points are exploited as new labels for the
pre-existing DSTC2 dataset to train the iTTD in a supervised
fashion. Source code, trained models and re-labeled dataset areavailable on GitHub1 .

In a human-machine dialog scenario, deciding the appropriate
time for the machine to take the turn is an open research problem. In contrast, humans engaged in conversations are able totimely decide when to interrupt the speaker for competitive or
non-competitive reasons. In state-of-the-art turn-by-turn dialog
systems the decision on the next dialog action is taken at the endof the utterance. In this paper, we propose a token-by-token prediction of the dialog state from incremental transcriptions of theuser utterance. To identify the point of maximal understandingin an ongoing utterance, we a) implement an incremental Dialog State Tracker which is updated on a token basis (iDST) b)
re-label the Dialog State Tracking Challenge 2 (DSTC2) dataset
and c) adapt it to the incremental turn-taking experimental scenario. The re-labeling consists of assigning a binary value toeach token in the user utterance that allows to identify the appropriate point for taking the turn. Finally, we implement an incremental Turn Taking Decider (iTTD) that is trained on these
new labels for the turn-taking decision. We show that the proposed model can achieve a better performance compared to a
deterministic handcrafted turn-taking algorithm.
Index Terms: Incremental Dialog State Tracking, Incremental
Turn-Taking Decider, Dialog Systems, Recurrent Neural Networks, Long Short-Term Memory

1. Introduction
The creation of dialog systems capable of holding conversations
at the same level of naturalness as those between people is stilla challenge and is far from being considered solved, in spiteof the numerous studies in this field. A conversation is to beconsidered productive when the emphasis is given not only tothe content that is conveyed, but also to the moment in which
this exchange takes place. Within this paper, we address bothissues by developing an incremental system capable of tracking
the content of the conversation and identifying the appropriate
moment for replying to the human counterpart.
In contrast to previous studies based on turn-by-turn systems [1, 2, 3, 4, 5, 6, 7, 8], which generate system utterances
only after detecting the end of the input from the user, we place
ourselves in a more challenging situation, where the system is
expected to generate an utterance after each token coming froman ongoing user utterance, thus relying on its incremental processing [9, 10].
While we believe that there should be a harmonious integration between prosodic signals and lexical features [11, 12],in this work we will focus only on the utterance transcription
in order to explore its potential in an incremental setting. Lexical features can be employed not only for determining the turn-

Copyright © 2019 ISCA

2. Task Definition
Our final goal is to create an iTTD (Subsection 2.2) capableof identifying the point within an ongoing user utterance from
where the state of the dialog relative to a specific turn no longer
varies, even if new tokens are subsequently provided. To reach
1 https://github.com/ahclab/iDST iTTD

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http://dx.doi.org/10.21437/Interspeech.2019-1826

To reflect the uncertainty of the ASR, a further fully connected
layer was added. This layer takes as input the concatenationof the confidence score and the previous embedding representation, thus creating a new embedding:it, we need an iDST (Subsection 2.1) to adapt the DSTC2 dataset
to the incremental setting (Subsection 4.1).
2.1. Incremental Dialog State Tracking
The dialog state at time t can be seen as a vector st ∈ C1 ×
C2 × · · · × Ck of k dialog state components where each component ci ∈ Ci = {v1 , . . . , vni } takes one of the ni values[9]. The goal of a dialog state tracker consists of mapping asequence of words w1 , . . . , wn in a specific dialog state st attime step t. This mapping is equivalent to the estimation ofp(st |w1 , . . . , wn ). The latter can therefore be seen as the estimation of a joint probability over components values:p(st |w1 , . . . , wn ) = p(c1 , . . . , ck |w1 , . . . , wn , θ)

w0t = emb plus(wt , at )This representation is then used in conjunction with the previous hidden state qt−1 by an LSTM function, which in turncreates a new hidden state as follows:
qt = LST M (w0t , qt−1 )

(7)

(1)

qt−1 = (ct−1 , ht−1 )

(8)

(2)

which contains a context vector c and a hidden vector h. This
last vector is then used to compute, by means of a softmax layer,
the probability distribution over all the possible values that agiven component can assume, which can be represented by:

where

or as a product of probabilities over component values:p(st |w1 , . . . , wn ) = Πki p(ci |w1 , . . . , wn , θi )(6)

if independence between components holds. In both cases, it isnecessary to determine the values of the θ parameters.
A turn-by-turn dialog state tracker estimates the dialog statep(st ) only after processing all the tokens in the user’s utterance.
In contrast, a token-by-token iDST attempts to estimate the same
dialog state p(st ) after each token, thus using prefixes of theentire user utterance.

pt = F (ht )(9)As for the iTTD, it must decide the ideal prefix point within
the user’s utterance for the dialog state prediction. In doing so,
it must use as few tokens as possible while trying to maintainthe same performance it would have if the entire utterance were
to be used. The decision of whether to take the turn can bemodeled as a probability distribution over two binary values bymeans of a softmax layer. The same formulation used in Equation (9) can be replicated for this purpose. Function F takes
as input the hidden vector ht estimated previously by the iDST,
but instead of predicting the value of a component, it predictsa binary value that indicates whether the turn should be taken.
Further details on data specifications are provided in Section 4.

2.2. Incremental Turn-Taking Decider
The dialog system must be able to determine the point when
it has enough information for taking the turn. Therefore, thissystem is able to estimate, for each token in a specific turn, thefollowing joint probability:p(take turn|st ) = p(0|st ) = p(c1 = 0, . . . , ck = 0|st , θ)(3)where st represents the current dialog state estimation and 0
indicates that there is no difference between the current dialog
state estimation and the one at the end of the user’s utterance. If
independence between components holds, the same probability
can be seen as:

4. Experiments
4.1. Dataset

In both cases, the complementary probability p(wait|st ) =p(1|st ) = 1 − p(take turn|st ) is also estimated.
The dichotomous decision made by the iTTD should, therefore, reflect the binary labeling that has been performed on the
pre-existing DSTC2 dataset according to the dialog state estimations of the iDST. This allows us to create a supervised versionof the iTTD. Details regarding the labeling function are provided in Subsection 4.1.

The DSTC2 dataset operates in the restaurant information domain. The dialog state is described by means of three macrocomponents: Goal, Requested and Method. The first macrocomponent is defined as the value assumed jointly by four
micro-components, namely Pricerange, Area, Name and Food.
The dataset is divided into three parts including train, dev
and test sets. A brief data analysis, taking into account only
the user utterances, is provided in Table 1. The train-dev OutOf-Vocabulary (OOV) rate is equal to 0.21, while the train-test
one is equal to 0.29. In terms of tokens distribution, all threedatasets follow the Zipf’s law [14] and have the same top-10
tokens set. Since the dataset in question does not provide any

3. Proposed Approach

Table 1: Dataset analysis based on user utterancesp(take turn|st ) = p(0|st ) = Πki p(ci = 0|st , θi )(4)

The iDST consists of an encoder-based classifier. It takes as input the set of tokens W , which consists of the concatenation of
the system output transcript and the ASR 1-best user utterance
hypothesis. Each token wt ∈ W is associated with a confidence
score at 2 . To be used by the model, each token is then mapped
to its corresponding fixed-size vector representation by meansof the embedding function:
wt = emb(wt )number of dialogs
number of tokens
max. seq. length
avg. tokens per turn
avg. turns per dialog

Train

Dev

Test

1612
896
28
3.88
4.93

506
720
25
3.92
5.45

1117
892
27
3.67
5.98

(5)
token-level feedback, it was necessary to find a way to propagate
this information from the turn-level to a more granular one. Oneof the ways in which this can be conducted is via re-labeling the

2 a assumes a fixed value for tokens in the system’s utterance. Fortthose that are part of the user’s utterance, the value assigned to the fullutterance by the ASR 1-best hypothesis is used.

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Table 2: iDST model layers and parameters
Layer
emb
emb plus
LSTM
classifier

ParameterValue

num embeddings
embedding dimin f eaturesout f eatures
input size
hidden sizein f eaturesout f eatures
activation

897
170
171
300
300
100
100
component dependent
log sof tmax or sigmoid

hence the accuracy value will be the same, the label function
assigns the 0 label.
(
1 if Acci not equal to Accn
(10)labeli =0 otherwise
Presumably, the first tokens of the user utterance will have label 1, while the last ones will have label 0. The transition pointfrom 1 to 0 indicates a suitable moment for taking the turn. Thislabeling method is therefore concerned with reflecting the imposed objective, namely minimizing the number of tokens used
to predict the dialog state while maintaining the same performance as if the entire utterance were to be used. These newlabels, therefore, can be employed for training the iTTD. It receives as input, for each token, the hidden vector ht comingfrom the iDST, and then tries to predict a binary value that reflects the labels in the new dataset, thus learning the transition
point from 1 to 0. If the iTTD confidence value on the predicted
0 label is greater than the imposed threshold, the ht vector isthen simply used by iDST in order to estimate the dialog state.

Table 3: iTTD model layers and parameters
Layer

Parameter

Value

classifierin f eaturesout f eatures
activation1002
log sof tmax

4.2. Experimental setting
The structure of the iDST and iTTD models, together with
their parameters, can be found in Table 2 and 3 respectively.
The classifier layer in Table 2 is a fully-connected layer andis the only one that varies according to the individual microcomponents of the dialog state. The criterion used for the training procedure is based on the cross-entropy loss [15]. Since weare in an incremental token-by-token setting, the value of theloss with respect to each turn is accumulated over the user tokens. This differs from a turn-by-turn approach, where the loss
value is computed only based on the prediction at the last tokenof the user turn. As an optimizer, we used the AMSGrad [16]variant of the Adam [17] algorithm with learning rate, β1 ,
β2 , eps, and weight decay set to 0.001, 0.9, 0.999, 1e−8 and
0 respectively. The metrics used to measure the tracker performance are accuracy and L2-norm. The first measures the raw
1-best accuracy of the ratio of turns in which the tracker’s hypothesis is correct. The second, on the other hand, measures
the L2-norm between the distribution of scores output by the
tracker and the label [13].

Figure 1: The frequency (z-axis) with which the turn is takenby the iTTD ASR(d = 0.85) model is computed based on theuser utterance length (y-axis) and the prefix point (x-axis) of
the utterance where the actual turn is taken. The frequency has
been clipped to 300 for plotting enhancement.

4.3. Experimental results
iDST and iTTD implement Equation (2) and (4) respectively,by creating an ensemble of independent models, each of whichrefers to one of the micro-components. The results obtainedby those models and a comparison with the reference one arereported in Table 4. As a comparative analysis, we also decided to train models which, instead of using the ASR 1-best
hypothesis (ASR suffix), use the manual transcription of theuser utterances (TRA suffix). Variables r and d in brackets indicate the ratio of used utterance and confidence of turn-taking
respectively. Since the length of the user utterances is not fixed,
it was necessary to shift the imposed 60% prefix point to thenearest token, which would inevitably alter this imposed percentage. The exact ratio has therefore been computed as theaverage of the actual percentage values, including shifts. Thisimplies that iDST ASR(r = 0.6) actually uses, as an average
percentage value, 61% and 68% of the user utterance on the
dev and test set respectively, instead of the 60% imposed value.
iDST {ASR, TRA}(r = 1.0) and iDST {ASR, TRA}(r = 0.6)
use respectively 100% and 60% of the user utterance for dialog
state estimation. iTTD {ASR, TRA}(d = 0.85) indicates that all

Figure 2: The accuracy (y-axis) of the three macro-components,
namely Goal, Method and Requested, is computed in an incremental fashion based on the prefix points (x-axis) of the user
utterance. The iTTD ASR(d = 0.85) model on average takes
the turn at 61% of the user utterance given that all microcomponents in the ensemble have a confidence of at least 85%
on the predicted 0 label (turn-taking).

dataset based on the accuracy of the dialog state prediction at
the token-level. Each token in the user utterance is assigned abinary value by means of Function (10). This label becomes 1
when the dialog state estimated by iDST at i-th token is different
from the one at the last n-th token. This also affects the accuracy
value, which therefore will change. If the estimation is correct,

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Table 4: Models with ASR suffix have been trained with the ASR 1-best user’s utterance hypothesis. The ones with TRA suffix have been
trained using the manual user utterance transcript. Bold indicates which one among iDST and iTTD prevailed in terms of accuracy.

Model
LecTrack [9]iDST ASR(r = 1.0)
iDST TRA(r = 1.0)
iDST ASR(r = 0.6)
iTTD ASR(d = 0.85)
iDST TRA(r = 0.6)
iTTD TRA(d = 0.85)

Goal
Acc.
L2

Dev
MethodAcc.
L2

Requested
Acc.
L2

Goal
Acc.
L2

Test
MethodAcc.
L2

Requested
Acc.
L2

0.63
0.64
0.87
0.57
0.59
0.77
0.80

0.90
0.90
0.94
0.89
0.88
0.93
0.92

0.96
0.96
0.99
0.86
0.91
0.88
0.91

0.62
0.63
0.82
0.56
0.58
0.73
0.76

0.92
0.92
0.94
0.91
0.91
0.94
0.93

0.96
0.97
0.99
0.86
0.91
0.88
0.91

0.74
0.53
0.23
0.61
0.60
0.34
0.31

0.19
0.17
0.10
0.18
0.19
0.11
0.12

0.08
0.07
0.02
0.23
0.16
0.18
0.15

0.75
0.56
0.30
0.62
0.61
0.39
0.37

0.15
0.13
0.09
0.14
0.15
0.10
0.11

0.07
0.06
0.02
0.21
0.15
0.18
0.15

micro-components in the ensemble must have a confidence of atleast 85% on the predicted 0 label (turn-taking). Setting the confidence threshold value to 85% causes the iTTD to take the turnon average at 61% (r = 0.61) of the user utterance, thus making it
comparable with the deterministic iDST {ASR, TRA}(r = 0.6).
The trend of the accuracy curves obtained by iDST ASR(r = 1.0)
relative to the three macro-components, together with the prefix point value selected on average by iTTD ASR(d = 0.85) for
the turn-taking decision, is shown in Figure 2. It can, therefore,
be observed that the 0.61 ratio prefix point chosen on averageby the iTTD ASR(d = 0.85) obtains a better or comparable performance with respect to the deterministic iDST ASR(r = 0.6)
which uses only 60% of the user utterance. The iDST TRA and
iTTD TRA models trained on the manual user transcript show
how the output of the ASR negatively affects the performanceof the models. Figure 1 instead shows in greater detail the frequency with which the iTTD ASR(d = 0.85) decides to take theturn based on the prefix point and the length of the user utterance. For instance, if we consider user utterances of length 2(e.g. ”phone number”, ”thank you”, ”good bye”, ”price range”),
it can be observed that a single word, which corresponds to 50%of the utterance, is often enough for the turn taking decision.

ance to process it and can, therefore, perform different actions
or provide feedback while listening to the human counterpart[23, 24, 25, 26]. To improve the efficiency of the dialogue, [27]defined a turn-taking phenomenon taxonomy, and showed that
only some phenomena are worth replicating. ASR and NLU
features have been exploited by [11] and [12] in order to detect the end of the turn in an incremental setting. They showedthat the combination of prosodic and lexical features can leadto promising results. A turn-taking model based on multitask
learning was proposed by [28], which also took into accountthe prediction of backchannels and fillers. An incremental turntaking model with active system barge-in was proposed by [29],who modeled the turn-taking problem as a Finite State Machine
and learned the turn-taking policy by means of reinforcement
learning. Our problem setting is similar to the one posed by[10], where they exploited the ASR and NLU for learning thepoint of maximal understanding of an ongoing user utterance.
In our case, we exploit the sole ASR 1-best hypothesis and there-labeled dataset, and try to predict the dialog state of the fullutterance before it has been completed.

5. Related WorkIn this paper, we proposed a methodology that exploits lexicalfeatures to build an automated system capable of estimating the
dialog state and the appropriate point for taking the turn in an incremental setting. An automatic re-labeling method that allows
for the propagation of turn-level feedback to a more granular
token-level one was introduced. Thanks to these labels we wereable to create a system that, on average, performs better than adeterministic decider concerning the turn-taking problem. The
decision of the threshold value regarding the confidence of the
iTTD model still remains a hyperparameter that must be manually set. This limitation opens up for future work that can focus
on this problem. In addition, it is evident that there is a needfor a metric capable of measuring the turn-taking performance,
which is specific to the incremental setting. As future work, wewould like to replace our supervised model for the decision on
turn-taking with a version based on reinforcement learning. The
aim is to analyze how much this methodology is able to manage
the adversative relationship between minimizing the number ofneeded tokens and maximizing the performance.

6. Conclusions

Capability of emulating humans behavior together with naturalness and effectiveness during a conversation, are characteristics
that automatic dialogue systems must have. In this sense, several studies have been conducted such as that of [18], which
considered user satisfaction through automatic analysis of behavior by measuring emotional states and providing a description as the interaction evolves. The user barge-in problem wasaddressed by [19], who developed a barge-in-able conversational dialog system that accepts user’s barge-in utterances. To
coordinate smooth exchange for speaking turns, [20] made useof prosodic, syntactic and gesture features for detecting suitable
feedback response locations in the user speech. To cope withincorrectly segmented utterances, [21] proposed an a posteriori restoration methodology. To better understand the behavior of the human counterpart, [22] tried to simulate the user bycreating a model that takes into account both her initial goal
and responses during the conversation. Despite the numerous
improvements introduced, these systems have a common denominator consisting of a relatively rigid structure due to theirturn-by-turn nature. For a system to be able to replicate human behaviour during a conversation, a paradigm shift is necessary, i.e. the system must be incremental. This means thatthe system does not need to wait for the end of the user utter-

7. Acknowledgments
The research leading to these results has received funding fromthe European Union — H2020 Programme under grant agreement 826266: COADAPT.

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8. References[19] K. Matsuyama, K. Komatani, T. Ogata, and H. G. Okuno, “Enabling a user to specify an item at any time during systemenumeration-item identification for barge-in-able conversational
dialogue systems,” in Tenth Annual Conference of the International Speech Communication Association, 2009.

[1] M. Henderson, B. Thomson, and S. Young, “Word-based dialog
state tracking with recurrent neural networks,” in Proceedings ofthe 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), 2014, pp. 292–299.

[20] R. Meena, G. Skantze, and J. Gustafson, “Data-driven models fortiming feedback responses in a map task dialogue system,” Computer Speech & Language, vol. 28, no. 4, pp. 903–922, 2014.

[2] R. Smith, “Comparative error analysis of dialog state tracking,”
in Proceedings of the 15th Annual Meeting of the Special Interest
Group on Discourse and Dialogue (SIGDIAL), 2014, pp. 300–
309.

[21] K. Komatani, N. Hotta, S. Sato, and M. Nakano, “User-adaptive aposteriori restoration for incorrectly segmented utterances in spoken dialogue systems,” Dialogue & Discourse, vol. 8, no. 2, pp.
206–224, 2017.

[3] K. Sun, L. Chen, S. Zhu, and K. Yu, “The sjtu system for dialog state tracking challenge 2,” in Proceedings of the 15th Annual
Meeting of the Special Interest Group on Discourse and Dialogue(SIGDIAL), 2014, pp. 318–326.

[22] I. Gur, D. Hakkani-Tür, G. Tür, and P. Shah, “User modelingfor task oriented dialogues,” CoRR, vol. abs/1811.04369, 2018.
[Online]. Available: http://arxiv.org/abs/1811.04369[4] B.-J. Lee, W. Lim, D. Kim, and K.-E. Kim, “Optimizing generative dialog state tracker via cascading gradient descent,” in Proceedings of the 15th Annual Meeting of the Special Interest Groupon Discourse and Dialogue (SIGDIAL), 2014, pp. 273–281.

[23] M. K. Tanenhaus, M. J. Spivey-Knowlton, K. M. Eberhard, andJ. C. Sedivy, “Integration of visual and linguistic information in
spoken language comprehension,” Science, vol. 268, no. 5217, pp.
1632–1634, 1995.

[5] O. Plátek, P. Bělohlávek, V. Hudeček, and F. Jurčı́ček, “Recurrent neural networks for dialogue state tracking,” arXiv preprint
arXiv:1606.08733, 2016.

[24] D. Schlangen and G. Skantze, “A general, abstract model of incremental dialogue processing,” in Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics,
2009, pp. 710–718.

[6] K. Yoshino, T. Hiraoka, G. Neubig, and S. Nakamura, “Dialogue
state tracking using long short term memory neural networks,” inProceedings of Seventh International Workshop on Spoken Dialog
Systems, 2016, pp. 1–8.

[25] K. Dohsaka and A. Shimazu, “System architecture for spoken utterance production in collaborative dialogue,” in Working Notes of
IJCAI 1997 Workshop on Collaboration, Cooperation and Conflict in Dialogue Systems, 1997.

[7] R. Masumura, T. Tanaka, A. Ando, R. Ishii, R. Higashinaka, and
Y. Aono, “Neural dialogue context online end-of-turn detection,”in Proceedings of the 19th Annual SIGdial Meeting on Discourseand Dialogue, 2018, pp. 224–228.

[26] J. Allen, G. Ferguson, and A. Stent, “An architecture for more
realistic conversational systems,” in Proceedings of the 6th international conference on Intelligent user interfaces. ACM, 2001,
pp. 1–8.

[8] N. G. Ward and D. DeVault, “Ten challenges in highly-interactive
dialog system,” in 2015 AAAI Spring Symposium Series, 2015.
[9] L. Zilka and F. Jurcicek, “Incremental lstm-based dialog state
tracker,” in 2015 Ieee Workshop on Automatic Speech Recognition and Understanding (Asru). IEEE, 2015, pp. 757–762.

[27] H. Khouzaimi, R. Laroche, and F. Lefevre, “Turn-taking phenomena in incremental dialogue systems,” in Proceedings of the 2015
Conference on Empirical Methods in Natural Language Processing, 2015, pp. 1890–1895.

[10] D. DeVault, K. Sagae, and D. Traum, “Can i finish?: learningwhen to respond to incremental interpretation results in interactive
dialogue,” in Proceedings of the SIGDIAL 2009 Conference: The
10th Annual Meeting of the Special Interest Group on Discourseand Dialogue. Association for Computational Linguistics, 2009,
pp. 11–20.

[28] K. Hara, K. Inoue, K. Takanashi, and T. Kawahara, “Prediction ofturn-taking using multitask learning with prediction of backchannels and fillers,” Listener, vol. 162, p. 364, 2018.
[29] T. Zhao, A. W. Black, and M. Eskenazi, “An incremental turntaking model with active system barge-in for spoken dialog systems,” in Proceedings of the 16th Annual Meeting of the Special
Interest Group on Discourse and Dialogue, 2015, pp. 42–50.

[11] M. Atterer, T. Baumann, and D. Schlangen, “Towards incremental
end-of-utterance detection in dialogue systems,” in Proceedings ofthe 22nd International Conference on Computational Linguistics,
2008.
[12] A. Raux and M. Eskenazi, “Optimizing endpointing thresholds
using dialogue features in a spoken dialogue system,” in Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue.
Association for Computational Linguistics, 2008, pp. 1–10.
[13] M. Henderson, B. Thomson, and J. D. Williams, “The second dialog state tracking challenge,” in Proceedings of the 15th Annual
Meeting of the Special Interest Group on Discourse and Dialogue(SIGDIAL), 2014, pp. 263–272.
[14] D. M. Powers, “Applications and explanations of zipf’s law,” inProceedings of the joint conferences on new methods in language
processing and computational natural language learning. Association for Computational Linguistics, 1998, pp. 151–160.
[15] R. Y. Rubinstein and D. P. Kroese, The cross-entropy method:a unified approach to combinatorial optimization, Monte-Carlosimulation and machine learning. Springer Science & BusinessMedia, 2013.
[16] S. J. Reddi, S. Kale, and S. Kumar, “On the convergence of adamand beyond,” arXiv preprint arXiv:1904.09237, 2019.
[17] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
[18] S. A. Chowdhury, E. A. Stepanov, and G. Riccardi, “Predictinguser satisfaction from turn-taking in spoken conversations.” in INTERSPEECH, 2016, pp. 2910–2914.

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