A case study of comparison of several methods
for corpus-based speech intention identification
Kazutaka Shimada

Kaoru Iwashita
Tsutomu Endo
Department of Artificial Intelligence,
Kyushu Institute of Technology
680-4 Iizuka Fukuoka 820-8502 Japan       

Abstract
Dialogue analysis is one of the most important tasks for human-machine interaction. It is important for dialogue systems to recognize the intention of an utterance and the intentional structure of a discourse. If a system identifies the intentionsof each utterance in a dialogue correctly, it
can recognize the structure of the dialogue.
This paper describes methods for intention
identification of an utterance. In this paper
we apply two methods to the task and evaluate the performance. The 1st method isbased on a similarity measure between an
input utterance and utterances in the corpus (Case examples). We compare several similarity measures in the experiment.
The 2nd method is based on the Maximum
Entropy (ME) method. We compare ourmethods with related work. In the experiment, the ME method produced the best
performance. For the coverage, the similarity based method with the inner productoutperformed the ME method. In addition,
we verify the effect of dependency relations between words and context information for this intention identification task.

1 Introduction
Dialogue analysis is one of the most importanttasks for human-machine interaction. Speech understanding systems have been developed to practical use recently. However, speech understanding is just one component of dialogue understanding systems. It is important for dialogue systems
to recognize the intention of an utterance and theintentional structure of a discourse. Recognitionof them leads to generation of more appropriate
model of plans for discourse and a solution of aproblem (Higashinaka et al., 2003, 2005). If asystem estimates the intentions of each utterance
in a dialogue correctly, it can recognize the structure of the dialogue. Some researchers have reported rule-based or example-based intention understanding methods (Kimura et al., 1998; Matsubara et al., 2002; Irie et al., 2003, 2004; Inuiet al., 2003). Kimura et al. (1998) have reported arule-based method for intention understanding. In
general, constructing rules for the method, however, is costly. Irie et al. (2003) have proposed
an example-based method for the task. They useda similarity measure and a sequence of utterances
for the intention understanding. Inui et al. (2003)
have proposed a method for classification of openended questionnaire texts based on surface expressions. They used the Maximum Entropy method
for the task.
In this paper we describe two methods for intention identification of an utterance. First, we explain a method based on a similarity measure. In
this method we compute a similarity between an
input utterance and utterances in a corpus (caseexamples). We compare several similarity measures. Next, we describe a method based on the
Maximum Entropy (ME) method. In addition, we
discuss features for the methods and the effect ofcontext information.
In Section 2, we explain our task, corpus andintentions. We prepare a manual for the tagging
process of the intentions and evaluate the reliability of the tagging based on the manual. In Section
3, we describe the two methods for the intention
identification. In Section 4, we evaluate the performance of each method and conclude this paper
in Section 5.

2 Intention and Corpus
In this section we explain the construction of a
corpus for our method and intentions in our task.
Furthermore, we evaluate the validity of a tagging
process for the corpus.

Proceedings of the 10th Conference of the Pacific Association for Computational Linguistics, pages 255–262.
This work is licensed under a Creative Commons Attribution 3.0 License (http://creativecommons.org/licenses/by/3.0/).

Table 1: The tagged corpus.
Teacher (T)Student (S)
Total

2.1

# of turns
1390
1449
2839

# of utterances
1509
1651
3160

CorpusIn this paper, we focus on a problem solving
and knowledge acquisition system based on coreference between drill texts and dialogue with ateacher (Endo and Kagawa, 1999; Shimada et al.,2007). We handled this task as a domain.
We collected a corpus consisting of simulated
dialogues for this domain. The corpus consist of
81 dialogues with 27 test subjects for person-tomachine. The simulated dialogues for person-tomachine were collected by Wizard of OZ method(Fraser and Gilbert, 1991). We transcribed the dialogues manually, and then tagged fillers and illformed expressions, such as self-correction andhesitation, in them. Figure 1 shows an example
of a drill text and a dialogue. The corpus contains
3160 utterances in 2839 turns (See Table 1). The
turn denotes “Si ” and “Ti ” in Figure 1.
2.2

S1: nanto onazi kazudesuka.
(What is the number equal to?)T1: kono waku no naka no chou wo kazoetekudasai.
(Count the number of butterflies in this frame)
S2: kore wo kazoerunodesune.
(May I count them?)
T2: soudesu.
(yes)
S3: dokokara kaado wo erabunodesuka.
(Where may I select from?)
T3: kokokara desu.
(From here.)S4: kono chou to kono kaado wo musubunodesune.
(May I link card(1) and butterfly(1)?).....
..... to be continued

Figure 1: An example of a drill text and a dialogue.

Intention
Therefore we use the traditional intention tags andexpand them in this paper.
For the corpus we tag an intention label for eachutterance. The number of intention labels is 12.
Some intentions possess an attribute. The 5 intentions out of 12 possess attributes. The total number of intentions including attribute patterns is 18.
Table 2 shows the intention tag set in our task.
We prepare a manual for the tagging process.
The manual contains the explanation of the intention labels, examples of utterance-intention pairs
and the guideline of the tagging. In this paper wetag one intention label to one utterance. Figure 2
shows an example of utterances and their intention
tags.

Various intention tags have been proposed in related studies (Araki et al., 1999; Irie et al., 2006;
Searle, 1969; Walker and Passonneau, 2001).
Walker and Passonneau (2001) have proposed a
dialogue act tagging scheme for a travel domain.
Their tags consisted of three dimension: (1) aspeech act dimension, (2) a task-subtask dimension, and (3) a conversational domain dimension.
Irie et al. (2006) have described a design of speechintention tags for an in-car spoken dialogue corpus. The tag set contained four layers: (1) Discourse act, (2) Action, (3) Object, and (4) Argument layers. These intention tags are specialized
for a task. As a result, their tags support to determine an operation of a speech understanding system. However, most of the intention tags designedby them are domain-specific tags.
On the other hand, there are familiar speechact labels and intentions, such as “request”, “assert” and “question” (Araki et al., 1999; Searle,1969). These intentions are more abstract thanthe domain-specific intentions. The purpose of ourstudy in this paper is comparison of several methods for identifying the intention of an utterance.

2.3

Evaluation of the Corpus

We need to consider the reliability of the tagging
process. For the evaluation the Kappa coefficient(κ ) has been adopted as a standard in the dialogue
processing (Cohen, 1960). The Kappa coefficientis computed as follows:
K

2

PO PE 
1 PE (1)

Table 2: Intention tags.
Label
INFREQ(INFormation REQuest)

INFSUP(INFormation SUPplement)

AGREXP(AGReement EXPression)

ACTREQ(ACTion REQuest)

CONREQ(CONfirmation REQuest)

Attribute
Howto
Request
Howto
Confirm
Correct
Response
Solve
InformOther
InformCorrect

ACTREP(ACTion REPort)DISEXP(DISagreement EXPression)

BEGDIA(BEGinning of DIAlogue)

ENDDIA(END of DIAlogue)

REQRES(REQuest for REStating)

ACTRES(ACTion REStating)

OTHER

Definition
Request for how to solve a problem
Request for information to solve a problem
Supplement for how to solve a problem
Supplement for information to solve a problem
Agreement for a confirmation request
Agreeable response
Request for an action to solve a problem
ACTREQ:solve with some hints
ACTREQ except above
Request for confirmation of information to solve a problem
Request for confirmation: right or wrong——

Report for an action request——

Expression of disagreement——

Beginning of a dialogue——

End of a dialogue——

Request for restating an utterance; e.g. paraphrasing——

Action restating an utterance——

An insignificant utterance; e.g. filler only

Table 3: Kappa coefficient.
PO
PE 
Kappa coefficient
Average

Experience
0.851
0.027
0.846

3 Identification of Intention

NoExperience
0.745
0.020
0.740
0.793

In this section we explain two methods for intention identification: similarity measures and the
maximum entropy method.
3.1

Similarity Measures

We compute a similarity between an input utterance and utterances in a corpus. We have already compared nine similarity measures: the inner product, some versions of the cosine measure,the Dice coefficient and the Jaccard coefficient(Iwashita et al., 2007) . In this paper we use threesimilarity measures that obtained high accuracy inthe nine similarity measures. The similarities are
the inner product (Inn), the Dice coefficient (Dice)and correspondence of morphemes (CorM). The
correspondence of morphemes (CorM) is a similarity measure reported in related work (Irie et al.,2003). These similarities are computed as follows:

where PO is the proportion of times the annotators agree and PE  is the proportion of times
that we would expect the annotators to agree bychance. If the Kappa coefficient is close to 1, thedegree of agreement is high.
We evaluated our tagging process with 10 test
subjects. 5 persons out of 10 have tagging experience for other utterances in our task. For this evaluation we extracted 6 dialogues (210 utterances)from the corpus randomly. Table 3 shows the results of the evaluation. We obtained κ 0 793 on
average. In general κ  0 7 indicates the substantial agreement of the tagging process (Araki et al.,1999). The result shows the high reliability of thetagging process with our manual.

InnUx  Uy 3T∑ xi  yi

i 1

(2)

Si : Dou sureba ii desuka? :: INFREQ:Howto(How should I do?)Ti : Mazu chou no kazu wo kazoete kudasai. :: ACTREQ:Solve(First, count the butterflies.)
Soshite onazi kazu no kaado wo sagashite kudasai :: ACTREQ:Solve(Then, search the card containing the same number.)Si1 : ..... to be continued.
Figure 2: An example of utterances and their intentions.
ZΛ d where U is an utterance. xi and yi are the value ofa word i in U respectively. For the inner product,
the value is a binary indicator, namely 1 (i existsin U) or 0 (otherwise). T is the number of vectors.
DiceUx  Uy 

2 ∑Ti 1 xi  yi
∑Ti 1 x2i  ∑Ti 1 y2i

(3)

2Mxy
Mx  Myfi c d  c (4)

2. construct a vector space for the identification
process

3.3

FeaturesWe employ two types of features for the vector
space model: (1) Bag-of-words (BOW) and (2)





c

4. output the intention that possesses the maximum probability computed from the estimated model

Maximum entropy modeling (ME) is one of thebest techniques for natural language processing(Berger et al., 1996). The principle of the ME isexpressed as follows:∑ λi c fi c d c

¼

3. estimate parameters by using the ME method

Maximum Entropy

exp

if exist d  i  0 and cotherwise

2. extract features from a corpus and then construct a training dataset

4. decide by a majority vote if some intentions
possess the same maximum similarity.

Z Λ d 

10

1. divide utterances into words by using themorphological analyzer ChaSen

3. compute the similarity between an input utterance (Ux ) and each utterance in a corpus(Uy )PΛcd i(7)where exist d  i is a indicator function. The valueis 1 in the case that a feature i exists in a documentd.
In this paper we use Amis, which is a parameter
estimator for maximum entropy models1 . We estimate parameters by using the generalized iterative
scaling algorithm.
The identification process with the ME is as follows:

1. divide utterances into words by using themorphological analyzer ChaSen (Matsumoto
et al., 1999)

1



¼

where Mx and My are the number of morphemes in
Ux and Uy . Mxy denotes the number of morphemes
matched between Ux and Uy . This computation isthe binary vector version of the Dice coefficient.
The identification process with the similarity
measures is as follows:

3.2

(6)



dc

where ZΛ d  is a normalization function. Λλ1  λn  are parameters for the model. These
parameters denote weights and significance ofeach feature. The parameter values are a set thatmaximizes the entropy concerning the classifier.
fi c d  c is a feature function that is defined as follows:

For the Dice coefficient, we use word frequency
for xi and yi in Ux and Uy .
CorM Ux  Uy 

∑ exp ∑ λi c fi c d c

1 
 

(5)

i

4

 

 



 

Table 4: Accuracy.

Q1
Q2
Q3
Ave

Inn
80.23
90.43
89.13
86.60

Similarity
Dice CorM
81.53 83.21
88.72 89.11
88.43 89.14
86.22 87.16

Table 5: Coverage.
ME
84.98
92.32
92.42
89.91

Q1
Q2
Q3
Ave

Coverage

ME
85.21
92.32
92.42
89.98

IncCorrectN(8)where N is the number of utterances in test data.
IncCorrect is the number of utterances that contained the correct intention at least one. Table
5 shows the experimental result. Since the ME
method usually output an unique intention as the
1st result, the difference between the accuracy andthe coverage was slight. For this criterion, the inner product produced the best performance. This
result denotes that the method with the inner product contains a possibility that it outperforms the
ME method essentially.
Regarding all the methods, the accuracy of the
problem Q1 was lower than those of Q2 and Q3.
On our observation, this tendency depends on the
difficulty of a problem. Q2 and Q3 were a simple problem, such as counting only. However Q1
needed to combine some processes3 . To solve a
difficult problem, a teacher and a student usuallyrequire enough interaction. Also, utterances in the
dialogue often tend to be long and complex sentences. As a result, the accuracy of intention identification decreased.
In this paper we employed a majority vote forthe final output of each similarity-based method.
However, the frequencies of each intention tag in
a corpus are not equable. For example, the tag
AGREXP:Response in the corpus was 1/10 of
the AGREXP:Correct tag. This denotes that themethod probably outputs the AGREXP:Correct
even if both of them exist in the intentions estimated by it. Therefore we introduced a normalization factor for the voting process.

4 Experiments
In this section, we evaluate the proposed methodwith our corpus. In this experiment, we evaluated the following points: (1) comparison of thesimilarity-based and the ME methods, (2) the coverage rates of each method, (3) the effectivenessof the sequence of utterances and (4) the effectiveness of dependency trees.
We evaluated this task with 81-fold cross validation, namely leave-one-out cross-validation. In
other words, we used 1 dialogue as test data and
80 dialogues as case examples from 81 dialogues
in our corpus. In this experiment we used the
FREQT system implemented by T. Kudo2 .
First we compared the similarity measures andthe ME-based methods. Table 4 shows the experimental result. The ME-based method outperformed the similarity-based methods. For thesimilarity-based methods, there was no significant
difference in them although the accuracy of the related work (CorM) was a little better than those of
the inner and the dice similarities.
However, the similarity-based methods often
contained several intentions that possessed thesame maximum similarity. We focused on the coverage rate of each method. The coverage was com    

Similarity
Dice CorM
84.38 86.19
92.59 93.00
90.11 90.82
89.03 90.00

puted as follows:

Dependency trees. We use all words as vectors
for the BOW. For the dependency trees, we analyze utterances by using Japanese dependency analyzer Cabocha (Kudo and Matsumoto, 2002). We
use frequent sub-trees for the vector space. We
employ the FREQT algorithm (Asai et al., 2002)
to extract them. The FREQT is an efficient pattern mining algorithm for discovering all frequent
tree patterns from a large collection of labeled ordered trees. It is based on the rightmost expansion,a technique to grow a tree by attaching new nodes
only on the rightmost branch of the tree.

2

Inn
88.74
96.01
93.96
92.92

vint # of int in the output# of int in the corpus

3 Actually Figure 1 was the Q1 in this experiment.

(9)

To
solve this problem, a student needs at least 3 steps: counting
butterflies, detecting the same number, and linking the cards.

  

5

Table 7: The utilization of tag sequence.

Table 6: The use of a normalization factor for Inn.
Majority Eq. (9)
Q1
80.23
45.30
Q2
90.43
62.36
Q3
89.13
55.61
Ave
86.60
54.42

Inn
Use
Q1
Q2
Q3
Ave

where int is an intention. Table 6 shows the experimental result. The normalization factor was noteffective although it was effective for some intentions, such as AGREXP:Response.
One of the solutions for this problem, i.e. low
frequency intention tags, is utilization of context
information, such as tag sequence. Next, we evaluated the effectiveness of the sequence of utterances. Generally, the intention of an utterance depends on the intentions of the previous utterances.
The history of intentions is one of the most important context in a dialogue. Matsubara et al. (2002)
and Irie et al. (2003) have reported the effectiveness of context information, i.e. intention n-gramprobability.
We applied the intention tag of the adjacent utterance in a corpus to our methods. First we extracted intention patterns that are the same as theprevious intention of the input utterance. Next wecomputed the similarity between the input and theutterances extracted from a corpus. We computed
a similarity measure (or a probability for ME) for
all utterances in the corpus if no intention patterns
exist in the corpus. Finally we decided by a majority vote if some intentions that possess the maximum similarity exist.
For the ME method, we generated a pair which
consists of the intention of the current utterance
and the intention of previous utterance. We regarded the pair as a new intention. For example,
assume that the intentions of Ui 1 and Ui are REQRES and ACTRES respectively. In this situation,
we consider the intention of Ui to be REQRESACTRES. We computed the maximum entropy
model by using these pair tags. In this experiment
we substituted this method for the extraction ofcandidates, namely utilization of a tag sequence.
Table 7 shows the experimental result. In the table, “ON” denotes that we used the tag sequence
for the method. In this experiment, we employed
the inner product as the similarity measure. As aresult, the utilization of tag sequence was ineffec-

OFF
80.23
90.43
89.13
86.60

ON
80.12
88.16
89.04
85.77

ME
OFF
84.98
92.32
92.42
89.91

ON
82.51
90.33
92.55
88.47

tive for our dataset4 . The accuracy decreased because utilizing a tag sequence led to the decreaseof candidates for the similarity calculation5 . Oneof the reasons is that our intention tags were simpler than that of the related work (Irie et al., 2003).
Their intention tags consist of four hierarchized relations. Although the accuracy for intention identification including lower-level intention, namely
the argument layer, increased by using context information, that for only the highest level intention
did not improve. Our intention tags are similar tothe highest level intention, namely the discourse
act, in the related work. Therefore utilization ofcontext information for this experiment did notlead to the improvement of the accuracy.
Finally we evaluated the effectiveness of dependency trees. We use the inner product with binary
vectors as the similarity measure. We compared
two patterns: the minimum support and length oftrees. The patterns are as follows:
Deps 1 the minimum support:
length: 2 to 5.

5, the pattern

Deps 2 the minimum support: 20, the patternlength: 2 to 5.
The results are shown in Table 8. The resultshows that using dependencies was ineffective.
For several dialogues, the method with dependencies, however, outperformed that with the BOW
only. Therefore we need the detail error analysis
to obtain higher accuracy.

5 Discussion and Conclusions
In this paper we described a method for identification of the intention of an utterance. Our methods
4 Needless to say, it was effective for some intentions, suchas  AGREXP:Response . However they did not lead to theimprovement of accuracy because they were minorities in thetest data
5 In this experiment, the coverage rate also decreased. The
coverage of the method with the inner product and context
information was 88.67%. The original coverage was 92.92%.
See Table 5.

6

Table 9: Accuracy with a small corpus.

Table 8: Accuracy with dependencies.
BOW
80.23
90.43
89.13
86.60

Q1
Q2
Q3
Ave

Deps1
80.31
89.29
88.45
86.21

Deps2
80.99
89.53
88.12
86.02

Intention Pairs
INFSUP:Confirm

REQRESAGREXP:Response

ACTRES

Q1
Q2
Q3
Ave

ACTREQ:Other

C5
C7

C8
ACTREPC1
BEGDIA

C2.......

C9
C4

C10

INFREQ
C6

C3.......

C11

CONREQ

Dice
74.79
85.87
84.78
81.81
(-4.41)

CorM
76.38
86.02
84.13
82.18
(-4.98)logue structure based on the analysis of our corpus. The dialogue structure is expressed by a transition network. Figure 3 shows a part of the relations and the dialogue structure The network contains the condition for the transition. We think thatthe relations and the structure are more useful thanthe context information that was described in Section 5 because they are global constraints in the dialogue. Effective utilization of relations between
intention labels is one future work.
One of the approaches for the improvement of
the accuracy is to incorporate high-level/abstract
knowledge and heuristics. Matsubara et al. (2002)
have used a word class for the similarity calculation. Also, in Japanese, clues of some intentions
often appear in the end of the sentence. To weightwords of the sentence-end might serve an important role in this task. Furthermore, we need tocompare the method in this paper with other similarity measures and statistical techniques, such asdecision tree learning (Irie et al., 2004) and Support Vector Machines (Vapnik, 1999). Integrationof several similarity measures and machine learning methods is one of exciting approaches, suchas the boosting algorithm (Freund and Schapier,1996).
For our method, the size of the corpus is oneof the most important problem. We evaluated themethods in the paper with a small dataset consisting of 27 dialogues. The result is shown in Table
9. The values in the parentheses in the table denotethe difference of the accuracy from 81 dialogues(See Table 4). The accuracy decreased in the casethat the corpus was small. Hence, in future studies,
we need to develop a corpus tool kit for constructing a large corpus. Evaluation for other domains
with the proposed method is also our future work.

ACTREP or AGREXP:Response
Dialogue Structure

Inn
76.47
85.10
84.67
82.08
(-4.52)

C4
C13

C8
C7
C1: Utterance from a teacher contains
a command: {write, link or enclose}.
C2: if not C1 or C3.

Figure 3: Intention pairs and A dialogue structure
in our corpus (Part of).

computed a similarity (or a probability) between
an input utterance and utterances in a corpus. In
this experiment, the Maximum Entropy method
outperformed similarity-based methods. However,the similarity-based method with the inner productsometimes contained the correct intention in the
output. This denotes a possibility that the methodwith some adjustments might improve the accuracy. For the final output of similarity-based methods, we employed a majority vote. Although weused a normalization factor for the voting process,it led to the decrease of the accuracy. In the experiment, tag sequence and dependencies were noteffective for the similarity calculation. However,they are effective for some intentions. Therefore
we need to consider the usage of them.
One of the solutions to improve the performance is utilization of dialogue/discourse structures. Kato et al. (2005) have reported a dialogueannotation of an in-car speech corpus. They expressed the dialogue structure as a binary tree. In
our task, intentions also contain a relation between
them: a pair relation. We are developing a dia-

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