Corpus-based Discourse Understanding in Spoken Dialogue Systems
Ryuichiro Higashinaka and Mikio Nakano and Kiyoaki Aikawa†
NTT Communication Science Laboratories
Nippon Telegraph and Telephone Corporation
3-1 Morinosato Wakamiya
Atsugi, Kanagawa 243-0198, Japan{rh,nakano}@atom.brl.ntt.co.jp, aik@idea.brl.ntt.co.jp

Abstract

This paper concerns the discourse understanding process in spoken dialogue systems. This process enables the system tounderstand user utterances based on thecontext of a dialogue. Since multiple candidates for the understanding result canbe obtained for a user utterance due tothe ambiguity of speech understanding, it
is not appropriate to decide on a single
understanding result after each user utterance. By holding multiple candidates
for understanding results and resolving theambiguity as the dialogue progresses, thediscourse understanding accuracy can be
improved. This paper proposes a method
for resolving this ambiguity based on statistical information obtained from dialogue corpora. Unlike conventional methods that use hand-crafted rules, the proposed method enables easy design of the
discourse understanding process. Experiment results have shown that a system that
exploits the proposed method performs
sufficiently and that holding multiple candidates for understanding results is effective.

†
Currently with the School of Media Science, Tokyo University of Technology, 1404-1 Katakuracho, Hachioji, Tokyo
192-0982, Japan.

1 Introduction
For spoken dialogue systems to correctly understanduser intentions to achieve certain tasks while conversing with users, the dialogue state has to be appropriately updated (Zue and Glass, 2000) after eachuser utterance. Here, a dialogue state means all
the information that the system possesses concerning the dialogue. For example, a dialogue state includes intention recognition results after each user
utterance, the user utterance history, the system utterance history, and so forth. Obtaining the user intention and the content of an utterance using only thesingle utterance is called speech understanding, andupdating the dialogue state based on both the previous utterance and the current dialogue state is called
discourse understanding. In general, the result ofspeech understanding can be ambiguous, because itis currently difficult to uniquely decide on a single
speech recognition result out of the many recognition candidates available, and because the syntactic and semantic analysis process normally produce
multiple hypotheses. The system, however, has to beable to uniquely determine the understanding result
after each user utterance in order to respond to theuser. The system therefore must be able to choose
the appropriate speech understanding result by referring to the dialogue state.
Most conventional systems uniquely determine
the result of the discourse understanding, i.e., thedialogue state, after each user utterance. However,multiple dialogue states are created from the current
dialogue state and the speech understanding resultscorresponding to the user utterance, which leads toambiguity. When this ambiguity is ignored, the dis-course understanding accuracy is likely to decrease.
Our idea for improving the discourse understanding
accuracy is to make the system hold multiple dialogue states after a user utterance and use succeeding utterances to resolve the ambiguity among dialogue states. Although the concept of combining
multiple dialogue states and speech understanding
results has already been reported (Miyazaki et al.,2002), they use intuition-based hand-crafted rules
for the disambiguation of dialogue states, which arecostly and sometimes lead to inaccuracy. To resolve
the ambiguity of dialogue states and reduce the costof rule making, we propose using statistical information obtained from dialogue corpora, which comprise dialogues conducted between the system andusers.
The next section briefly illustrates the basic architecture of a spoken dialogue system. Section 3
describes the problem to be solved in detail. Thenafter introducing related work, our approach is described with an example dialogue. After that, we
describe the experiments we performed to verify ourapproach, and discuss the results. The last section
summarizes the main points and mentions futurework.

2 Discourse Understanding
Here, we describe the basic architecture of a spoken
dialogue system (Figure 1). When receiving a user
utterance, the system behaves as follows.
1. The speech recognizer receives a user utterance
and outputs a speech recognition hypothesis.
2. The language understanding component receives the speech recognition hypothesis. The
syntactic and semantic analysis is performed
to convert it into a form called a dialogue
act. Table 1 shows an example of a dialogue
act. In the example, “refer-start-and-end-time”
is called the dialogue act type, which briefly
describes the meaning of a dialogue act, and“start=14:00” and “end=15:00” are add-on information.1
1
In general, a dialogue act corresponds to one sentence.
However, in dialogues where user utterances are unrestricted,
smaller units, such as phrases, can be regarded as dialogue acts.

Speech Recognition
Hypothesis
SpeechRecognizer

SpeechSynthesizer

Dialogue Act

Language
Understanding
Component

Dialogue
Manager

Discourse
Understanding
Component
Refer

Refer

UpdateUpdateDialogue
State

Figure 1: Architecture of a spoken dialogue system.
3. The discourse understanding component receives the dialogue act, refers to the current dialogue state, and updates the dialogue state.
4. The dialogue manager receives the current dialogue state, decides the next utterance, and outputs the next words to speak. The dialogue stateis updated at the same time so that it contains
the content of system utterances.
5. The speech synthesizer receives the output ofthe dialogue manager and responds to the user
by speech.
This paper deals with the discourse understanding component. Since we are resolving the ambiguity of speech understanding from the discourse
point of view and not within the speech understanding candidates, we assume that a dialogue state is
uniquely determined given a dialogue state and thenext dialogue act, which means that a dialogue act
is a command to change a dialogue state. We also
assume that the relationship between the dialogue
act and the way to update the dialogue state can beeasily described without expertise in dialogue system research. We found that these assumptions arereasonable from our experience in system development. Note also that this paper does not separately
deal with reference resolution; we assume that it isperformed by a command. A speech understanding
result is considered to be equal to a dialogue act inthis article.
In this paper, we consider frames as representations of dialogue states. To represent dialogue states,
plans have often been used (Allen and Perrault,
1980; Carberry, 1990). Traditionally, plan-based
discourse understanding methods have been implemented mostly in keyboard-based dialogue systems,

User Utterance
Dialogue Act“from two p.m. to three p.m.”[act-type=refer-start-and-endtime, start=14:00, end=15:00]

Table 1: A user utterance and the corresponding dialogue act.
although there are some recent attempts to apply
them to spoken dialogue systems as well (Allen etal., 2001; Rich et al., 2001); however, considering
the current performance of speech recognizers andthe limitations in task domains, we believe framebased discourse understanding and dialogue management are sufficient (Chu-Carroll, 2000; Seneff,
2002; Bobrow et al., 1977).

3 Problem
Most conventional spoken dialogue systems
uniquely determine the dialogue state after a user
utterance. Normally, however, there are multiple
candidates for the result of speech understanding,which leads to the creation of multiple dialogue
state candidates. We believe that there are cases
where it is better to hold more than one dialogue
state and resolve the ambiguity as the dialogue
progresses rather than to decide on a single dialogue
state after each user utterance.
As an example, consider a piece of dialogue inwhich the user utterance “from two p.m.” has been
misrecognized as “uh two p.m.” (Figure 2). Figure 3 shows the description of the example dialogue in detail including the system’s inner states,
such as dialogue acts corresponding to the speech
recognition hypotheses2 and the intention recognition results.3 After receiving the speech recognition hypothesis “uh two p.m.,” the system cannot
tell whether the user utterance corresponds to a dialogue act specifying the start time or the end time
(da1,da2). Therefore, the system tries to obtainfurther information about the time. In this case,the system utters a backchannel to prompt the next
user utterance to resolve the ambiguity from the discourse.4 At this stage, the system holds two dialogue
2

In this example, for convenience of explanation, the n-best
speech recognition input is not considered.
3
An intention recognition result is one of the elements of a
dialogue state.
4
A yes/no question may be an appropriate choice as well.





S1

:

U1
S2
U2
S3
U3

:
:
:
:
:

what time would you like to reserve a
meeting room?
from two p.m. [uh two p.m.]
uh-huhto three p.m. [to three p.m.]from two p.m. to three p.m.?
yes [yes]

Figure 2: Example dialogue.
(S means a system utterance and U a user utterance.
Recognition results are enclosed in square brackets.)states having different intention recognition results(ds1,ds2). The next utterance, “to three p.m.,” is
one that uniquely corresponds to a dialogue act specifying the end time (da3), and thus updates the twocurrent dialogue states. As a result, two dialogue
states still remain (ds3,ds4). If the system can tellthat the previous dialogue act was about the start
time at this moment, it can understand the user intention correctly. The correct understanding result,ds3, is derived from the combination of ds1 andda3, where ds1 is induced by ds0 and da1. As
shown here, holding multiple understanding results
can be better than just deciding on the best speech
understanding hypothesis and discarding other possibilities.
In this paper, we consider a discourse understanding component that deals with multiple dialogue
states. Such a component must choose the best combination of a dialogue state and a dialogue act out ofall possibilities. An appropriate scoring method forthe dialogue states is therefore required.

4 Related Work
Nakano et al. (1999) proposed a method that holds
multiple dialogue states ordered by priority to deal
with the problem that some utterances convey meaning over several speech intervals and that the understanding result cannot be determined at each interval end. Miyazaki et al. (2002) proposed a method
combining Nakano et al.’s (1999) method and n-best
recognition hypotheses, and reported improvement
in discourse understanding accuracy. They used a
metric similar to the concept error rate for the evalu-

[System utterance (S1)]“What time would you like to reserve a meetingroom?”[Dialogue act] [act-type=ask-time][Intention recognition result candidates]
1. [room=nil, start=nil, end=nil] (ds0)↓[User utterance (U1)]“From two p.m.”[Speech recognition hypotheses]
1. “uh two p.m.”[Dialogue act candidates]
1. [act-type=refer-start-time,time=14:00] (da1)2. [act-type=refer-end-time,time=15:00] (da2)[Intention recognition result candidates]
1. [room=nil, start=14:00, end=nil](ds1, induced from ds0 and da1)
2. [room=nil, start=nil, end=14:00](ds2, induced from ds0 and da2)↓
[System utterance (S2)] “uh-huh”[Dialogue act] [act-type=backchannel]↓[User utterance (U2)]“To three p.m.”[Speech recognition hypotheses]
1. “to three p.m.”[Dialogue act candidates]
1. [act-type=refer-end-time, time=15:00] (da3)[Intention recognition result candidates]
1. [room=nil, start=14:00, end=15:00](ds3, induced from ds1 and da3)
2. [room=nil, start=nil, end=15:00](ds4, induced from ds2 and da3)↓
[System utterance (S3)]“from two p.m. to three p.m.?”[Dialogue act]
[act-type=confirm-time,start=14:00, end=15:00]↓[User utterance (U3)] “yes”[Speech recognition hypotheses]
1. “yes”[Dialogue act candidates]
1. [act-type=acknowledge][Intention recognition result candidates]
1. [room=nil, start=14:00, end=15:00]
2. [room=nil, start=nil, end=15:00]

Figure 3: Detailed description of the understandingof the example dialogue.

ation of discourse accuracy, comparing reference dialogue states with hypothesis dialogue states. Boththese methods employ hand-crafted rules to scorethe dialogue states to decide the best dialogue state.
Creating such rules requires expert knowledge, andis also time consuming.
There are approaches that propose statistically estimating the dialogue act type from several previous
dialogue act types using N-gram probability (Nagata
and Morimoto, 1994; Reithinger and Maier, 1995).
Although their approaches can be used for disambiguating user utterance using discourse information, they do not consider holding multiple dialogue
states.
In the context of plan-based utterance understanding (Allen and Perrault, 1980; Carberry, 1990),
when there is ambiguity in the understanding result of a user utterance, an interpretation best suited
to the estimated plan should be selected. In addition, the system must choose the most plausibleplans from multiple possible candidates. Although
we do not adopt plan-based representation of dialogue states as noted before, this problem is close towhat we are dealing with. Unfortunately, however,it seems that no systematic ways to score the candidates for disambiguation have been proposed.

5 Approach
The discourse understanding method that we propose takes the same approach as Miyazaki et al.
(2002). However, our method is different in that,when ordering the multiple dialogue states, the statistical information derived from the dialogue corpora is used. We propose using two kinds of statistical information:
1. the probability of a dialogue act type sequence,and
2. the collocation probability of a dialogue state
and the next dialogue act.
5.1 Statistical Information
Probability of a dialogue act type sequence
Based on the same idea as Nagata and Morimoto(1994) and Reithinger and Maier (1995), we use theprobability of a dialogue act type sequence, namely,
the N-gram probability of dialogue act types. System utterances and the transcription of user utterances are both converted to dialogue acts using a dialogue act conversion parser, then the N-gram probability of the dialogue act types is calculated.

#
1.
2.
3.
4.
5.
6.
7.

explanation
whether slots asked previously by the system
are changed
whether slots being confirmed are changed
whether slots already confirmed are changed
whether the dialogue act fills slots that do nothave values
whether the dialogue act tries changing slotsthat have values
when 5 is true, whether slot values are notchanged as a result
whether the dialogue act updates the initial
dialogue state 5

Table 2: Seven binary attributes to classify collocation patterns of a dialogue state and the next dialogue act.

Collocation probability of a dialogue state andthe next dialogue act From the dialogue corpora,dialogue states and the succeeding user utterances
are extracted. Then, pairs comprising a dialogue
state and a dialogue act are created after converting user utterances into dialogue acts. Contrary to
the probability of sequential patterns of dialogue act
types that represents a brief flow of a dialogue, thiscollocation information expresses a local detailed
flow of a dialogue, such as dialogue state changes
caused by the dialogue act. The simple bigram of
dialogue states and dialogue acts is not sufficientdue to the complexity of the data that a dialogue
state possesses, which can cause data sparsenessproblems. Therefore, we classify the ways that dialogue states are changed by dialogue acts into 64
classes characterized by seven binary attributes (Table 2) and compute the occurrence probability ofeach class in the corpora. We assume that the understanding result of the user intention contained ina dialogue state is expressed as a frame, which iscommon in many systems (Bobrow et al., 1977). A
frame is a bundle of slots that consist of attributevalue pairs concerning a certain domain.

5
The first user utterance should be treated separately, because the system’s initial utterance is an open question leading
to an unrestricted utterance of a user.

5.2

Scoring of Dialogue Acts

Each speech recognition hypothesis is converted toa dialogue act or acts. When there are several dialogue acts corresponding to a speech recognition
hypothesis, all possible dialogue acts are created asin Figure 3, where the utterance “uh two p.m.” produces two dialogue act candidates. Each dialogue
act is given a score using its linguistic and acoustic scores. The linguistic score represents the grammatical adequacy of a speech recognition hypothesis from which the dialogue act originates, and theacoustic score the acoustic reliability of a dialogue
act. Sometimes, there is a case that a dialogue act
has such a low acoustic or linguistic score and that
it is better to ignore the act. We therefore create a
dialogue act called null act, and add this null act toour list of dialogue acts. A null act is a dialogue act
that does not change the dialogue state at all.
5.3

Scoring of Dialogue States

Since the dialogue state is uniquely updated by a dialogue act, if there are l dialogue acts derived fromspeech understanding and m dialogue states, m × l
new dialogue states are created. In this case, we define the score of a dialogue state St+1 as
St+1 = St + α · sact + β · sngram + γ · scolwhere St is the score of a dialogue state just before
the update, sact the score of a dialogue act, sngram
the score concerning the probability of a dialogue
act type sequence, scol the score concerning the collocation probability of dialogue states and dialogue
acts, and α, β, and γ are the weighting factors.
5.4

Ordering of Dialogue States

The newly created dialogue states are ordered basedon the score. The dialogue state that has the bestscore is regarded as the most probable one, and thesystem responds to the user by referring to it. The
maximum number of dialogue states is needed inorder to drop low-score dialogue states and thereby
perform the operation in real time. This dropping
process can be considered as a beam search in viewof the entire discourse process, thus we name themaximum number of dialogue states the dialogue
state beam width.

6 Experiment
6.1

Extracting Statistical Information from Dialogue Corpus
Dialogue Corpus We analyzed a corpus of dialogues between naive users and a Japanese spoken
dialogue system, which were collected in acoustically insulated booths. The task domain was meeting room reservation. Subjects were instructed toreserve a meeting room on a certain date from a certain time to a certain time. As a speech recognitionengine, Julius3.1p1 (Lee et al., 2001) was used with
its attached acoustic model. For the language model,
we used a trigram trained from randomly generated
texts of acceptable phrases. For system response,
NTT’s speech synthesis engine FinalFluet (Takano
et al., 2001) was used. The system had a vocabularyof 168 words, each registered with a category anda semantic feature in its lexicon. The system usedhand-crafted rules for discourse understanding. The
corpus consists of 240 dialogues from 15 subjects(10 males and 5 females), each one performing 16
dialogues. Dialogues that took more than three minutes were regarded as failures. The task completion
rate was 78.3% (188/240).
Extraction of Statistical Information From thetranscription, we created a trigram of dialogue act
types using the CMU-Cambridge Toolkit (Clarkson
and Rosenfeld, 1997). Figure 3 shows an exampleof the trigram information starting from {refer-starttime backchannel}. The bigram information used
for smoothing is also shown. The collocation probability was obtained from the recorded dialogue states
and the transcription following them. Out of 64 possible patterns, we found 17 in the corpus as shown in
Figure 4. Taking the case of the example dialogue in
Figure 3, it happened that the sequence {refer-starttime backchannel refer-end-time} does not appear in
the corpus; thus, the probability is calculated basedon the bigram probability using the backoff weight,
which is 0.006. The trigram probability for {referend-time backchannel refer-end-time} is 0.031.
The collocation probability of the sequence ds1+ da3 → ds3 fits collocation pattern 12, where aslot having no value was changed. The sequenceds2 + da3 → ds4 fits collocation pattern 17, wherea slot having a value was changed to have a different value. The probabilities were 0.155 and 0.009,

dialogue act type sequence (trigram)probabilityscore-1.0852-2.0445-0.8633-2.0445-1.7790-0.4009-0.8633-0.7172

refer-start-time backchannel backchannelrefer-start-time backchannel ask-daterefer-start-time backchannel ask-start-timerefer-start-time backchannel request
refer-start-time backchannel refer-day
refer-start-time backchannel refer-monthrefer-start-time backchannel refer-room
refer-start-time backchannel refer-start-time
dialogue act type sequence(bigram)refer-start-time backchannelrefer-end-time backchannel
backchannel refer-end-timebackoff
weight-1.13370.4570-0.5567

probabilityscore-0.7928-0.6450-1.0716

Table 3: An example of bigram and trigram of dialogue act types with their probability score in common logarithm.
#
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.

collocation
pattern
011100101100100000000
1000100
1011000
0011000
0000100
0110100
0011001
00100100000001
0001000
1001000
0010100
1001001
0001001
0000010occurrence
probability
0.001
0.053
0.273
0.001
0.005
0.036
0.047
0.041
0.010
0.016
0.064
0.155
0.043
0.061
0.001
0.186
0.009

Table 4: The 17 collocation patterns and their occurrence probabilities. See Figure 2 for the detailof binary attributes. Attributes 1-7 are ordered fromleft to right.
respectively. By the simple adding of the two probabilities in common logarithms in each case, ds3 has
the probability score -3.015 and ds4 -3.549, suggesting that the sequence ds3 is the most probablediscourse understanding result after U2.
6.2 Verification of our approach
To verify the effectiveness of the proposed approach, we built a Japanese spoken dialogue systemin the meeting reservation domain that employs theproposed discourse understanding method and performed dialogue experiments.
The speech recognition engine was Julius3.3p1(Lee et al., 2001) with its attached acoustic models.
For the language model, we made a trigram fromthe transcription obtained from the corpora. The
system had a vocabulary of 243. The recognitionengine outputs 5-best recognition hypotheses. Thistime, values for sact , sngram , scol are the logarithmof the inverse number of n-best ranks,6 the log likelihood of dialogue act type trigram probability, andthe common logarithm of the collocation probability, respectively. For the experiment, weighting factors are all set to one (α = β = γ = 1). The dialogue state beam width was 15. We collected 256
dialogues from 16 subjects (7 males and 9 females).
The speech recognition accuracy (word error rate)
was 65.18%. Dialogues that took more than fiveminutes were regarded as failures. The task completion rate was 88.3% (226/256).7
From all user speech intervals, the number oftimes that dialogue states below second place became first place was 120 (7.68%), showing a relative
frequency of shuffling within the dialogue states.
6.3

Effectiveness of Holding Multiple Dialogue
StatesThe main reason that we developed the proposed
corpus-based discourse understanding method wasthat it is difficult to manually create rules to deal
with multiple dialogue states. It is yet to be examined, however, whether holding multiple dialogue
states is really effective for accurate discourse understanding.
To verify that holding multiple dialogue states iseffective, we fixed the speech recognizer’s output to
1-best, and studied the system performance changes
when the dialogue state beam width was changed
from 1 to 30. When the dialogue state beam width is
too large, the computational cost becomes high andthe system cannot respond in real time. We therefore
selected 30 for empirical reasons.
The task domain and other settings were the same6
In this experiment, only the acoustic score of a dialogue act
was considered.
7
It should be noted that due to the creation of an enormous
number of dialogue states in discourse understanding, the proposed system takes a few seconds to respond after the user input.

as in the previous experiment except for the dialogue
state beam width changes. We collected 448 dialogues from 28 subjects (4 males and 24 females),each one performing 16 dialogues. Each subject was
instructed to reserve the same meeting room twice,once with the 1-beam-width system and again with
30-beam-width system. The order of what room toreserve and what system to use was randomized.
The speech recognition accuracy was 69.17%. Dialogues that took more than five minutes were regarded as failures. The task completion rates for the
1-beam-width system and the 30-beam-width system were 88.3% and 91.0%, and the average taskcompletion times were 107.66 seconds and 95.86
seconds, respectively. A statistical hypothesis test
showed that times taken to carry out a task with the
30-beam-width system are significantly shorter thanthose with the 1-beam-width system (Z = −2.01,
p < .05). In this test, we used a kind of censoredmean computed by taking the mean of the times
only for subjects that completed the tasks with both
systems. The population distribution was estimatedby the bootstrap method (Cohen, 1995). It may bepossible to evaluate the discourse understanding bycomparing the best dialogue state with the reference
dialogue state, and calculate a metric such as the
CER (concept error rate) as Miyazaki et al. (2002)
do; however it is not clear whether the discourse
understanding can be evaluated this way, since it is
not certain whether the CER correlates closely withthe system’s performance (Higashinaka et al., 2002).
Therefore, this time, we used the task completiontime and the task completion rate for comparison.

7 Discussion
Cost of creating the discourse understanding
component The best task completion rate in the experiments was 91.0% (the case of 1-best recognition
input and a 30 dialogue state beam width). This highrate suggests that the proposed approach is effective
in reducing the cost of creating the discourse understanding component in that no hand-crafted rules
are necessary. For statistical discourse understanding, an initial system, e.g., a system that employs
the proposed approach with only sact for scoring the
dialogue states, is needed in order to create the dialogue corpus; however, once it has been made, thecreation of the discourse understanding componentrequires no expert knowledge.
Effectiveness of holding multiple dialogue states
The result of the examination of dialogue state beamwidth changes suggests that holding multiple dialogue states shortens the task completion time. As
far as task-oriented spoken dialogue systems areconcerned, holding multiple dialogue states contributes to the accuracy of discourse understanding.

8 Summary and Future Work
We proposed a new discourse understanding methodthat orders multiple dialogue states created frommultiple dialogue states and the succeeding speech
understanding results based on statistical information obtained from dialogue corpora. The results ofthe experiments show that our approach is effective
in reducing the cost of creating the discourse understanding component, and the advantage of keeping
multiple dialogue states was also shown.
There still remain several issues that we need to
explore. These include the use of statistical information other than the probability of a dialogue act typesequence and the collocation probability of dialogue
states and dialogue acts, the optimization of weighting factors α, β, γ, other default parameters that weused in the experiments, and more experiments inlarger domains. Despite these issues, the present results have shown that our approach is promising.

Acknowledgements
We thank Dr. Hiroshi Murase and all members of the
Dialogue Understanding Research Group for useful
discussions. Thanks also go to the anonymous reviewers for their helpful comments.

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