Analysis System of Speech Acts and Discourse Structures Using 
Maximum Entropy Model* 
Won Seug Choi, Jeong-Mi Cho and Jungyun Seo 
Dept. of Computer Science, Sogang University 
Sinsu-dong 1, Mapo-gu 
Seoul, Korea, 121-742 
{dolhana, jmcho} @nlprep.sogang.ac.kr, seojy@ccs.sogang.ac.kr 
Abstract 
We propose a statistical dialogue analysis 
model to determine discourse structures as 
well as speech acts using maximum entropy 
model. The model can automatically acquire 
probabilistic discourse knowledge from a 
discourse tagged corpus to resolve 
ambiguities. We propose the idea of tagging 
discourse segment boundaries to represent 
the structural information of discourse. 
Using this representation we can effectively 
combine speech act analysis and discourse 
structure analysis in one framework. 
Introduction 
To understand a natural language dialogue, a 
computer system must be sensitive to the 
speaker's intentions indicated through utterances. 
Since identifying the speech acts of utterances is 
very important to identify speaker's intentions, it 
is an essential part of a dialogue analysis system. 
It is difficult, however, to infer the speech act 
from a surface utterance since an utterance may 
represent more than one speech act according to 
the context. Most works done in the past on the 
dialogue analysis has analyzed speech acts based 
on knowledge such as recipes for plan inference 
and domain specific knowledge (Litman (1987), 
Caberry (1989), Hinkelman (1990), Lambert 
(1991), Lambert (1993), Lee (1998)). Since 
these knowledge-based models depend on costly 
hand-crafted knowledge, these models are 
difficult to be scaled up and expanded to other 
domains. 
Recently, machine learning models using a 
discourse tagged corpus are utilized to analyze 
speech acts in order to overcome such problems 
(Nagata (1994a), Nagata (1994b), Reithinger 
(1997), Lee (1997), Samuel (1998)). Machine 
learning offers promise as a means of 
associating features of utterances with particular 
speech acts, since computers can automatically 
analyze large quantities of data and consider 
many different feature interactions. These 
models are based on the features such as cue 
phrases, change of speaker, short utterances, 
utterance length, speech acts tag n-grams, and 
word n-grams, etc. Especially, in many cases, 
the speech act of an utterance influenced by the 
context of the utterance, i.e., previous utterances. 
So it is very important to reflect the information 
about the context to the model. 
Discourse structures of dialogues are usually 
represented as hierarchical structures, which 
reflect embedding sub-dialogues (Grosz (1986)) 
and provide very useful context for speech act 
analysis. For example, utterance 7 in Figure 1 
has several surface speech acts such as 
acknowledge, inform, and response. Such an 
ambiguity can be solved by analyzing the 
context. If we consider the n utterances linearly 
adjacent to utterance 7, i.e., utterances 6, 5, etc., 
as context, we will get acknowledge or inform 
with high probabilities as the speech act of 
utterance 7. However, as shown in Figure 1, 
utterance 7 is a response utterance to utterance 2 
that is hierarchically recent to utterance 7 
according to the discourse structure of the 
dialogue. If we know the discourse structure of 
the dialogue, we can determine the speech act of 
utterance 7 as response. 
* This work was supported by KOSEF under the 
contract 97-0102-0301-3. 
230 
Some researchers have used the structural 
information of discourse to the speech act 
analysis (Lee (1997), Lee (1998)). It is not, 
however, enough to cover various dialogues 
since they used a restricted rule-based model 
such as RDTN (Recursive Dialogue Transition 
Networks) for discourse structure analysis. Most 
of the previous related works, to our knowledge, 
tried to determine the speech act of an utterance, 
but did not mention about statistical models to 
determine the discourse structure of a dialogue. 
I )User : I would like Io reserve a room. 
2) Agent : What kind of room do you want? 
3) User : What kind of room do you have'? 
4) Agent : We have single mid double rooms. 
5) User : How much are those rooms? 
6) Agent : Single costs 30,000 won and double ~SlS 40,000 WOll. 
7) User : A single room. please. 
request 
ask-ref 
ask-ref 
response 
ask-tel 
response 
acknowledge 
inform 
r~mmse 
Figure 1 : An example of a dialogue with speech acts 
In this paper, we propose a dialogue analysis 
model to determine both the speech acts of 
utterances and the discourse structure of a 
dialogue using maximum entropy model. In the 
proposed model, the speech act analysis and the 
discourse structure analysis are combined in one 
framework so that they can easily provide 
feedback to each other. For the discourse 
structure analysis, we suggest a statistical model 
with discourse segment boundaries (DSBs) 
similar to the idea of gaps suggested for a 
statistical parsing (Collins (1996)). For training, 
we use a corpus tagged with various discourse 
knowledge. To overcome the problem of data 
sparseness, which is common for corpus-based 
works, we use split partial context as well as 
whole context. 
After explaining the tagged dialogue corpus we 
used in section 1, we discuss the statistical 
models in detail in section 2. In section 3, we 
explain experimental results. Finally, we 
conclude in section 4. 
1 Discourse tagging 
In this paper, we use Korean dialogue corpus 
transcribed from recordings in real fields such as 
hotel reservation, airline reservation and tour 
reservation. This corpus consists of 528 
dialogues, 10,285 utterances (19.48 utterances 
per dialogue). Each utterance in dialogues is 
manually annotated with discourse knowledge 
such as speaker (SP), syntactic pattern (ST), 
speech acts (SA) and discourse structure (DS) 
information. Figure 2 shows a part of the 
annotated dialogue corpus ~. SP has a value 
either "User" or "Agent" depending on the 
speaker. 
/SPAJser 
/ENh'm a student and registered/br a 
language course at University of Georgia in 
U.S. 
ISTl\[decl,be,present,no,none,none\] 
/SA/introducing -oneself 
/DS/\[2I 
/SP/User 
~9_. 
/EN/I have sa)me questions about lodgings. 
IST/Idecl,paa.presenl,no,none,nonel 
/SA/ask-ref 
~DS/121 
--> Continue 
/SP/Agent 
/EN/There is a dormitory in Universily of 
Georgia lot language course students. 
ISTIIdecl.pvg,present,no,none.none\] 
/SA/response 
/DS/\[21 
/SPAJser 
/ENfrhen, is meal included in tuilion lee? 
/ST/¿yn quest.pvg ,present.no.none ,then I 
/SA/ask-if 
/DS/12. I I 
Figure 2: A part of the annotated dialogue corpus 
The syntactic pattern consists of the selected 
syntactic features of an utterance, which 
approximate the utterance. In a real dialogue, a 
speaker can express identical contents with 
different surface utterances according to a 
personal linguistic sense. The syntactic pattern 
generalizes these surface utterances using 
syntactic features. The syntactic pattern used in 
(Lee (1997)) consists of four syntactic features 
such as Sentence Type, Main-Verb, Aux-Verb 
and Clue-Word because these features provide 
strong cues to infer speech acts. We add two 
more syntactic features, Tense and Negative 
Sentence, to the syntactic pattern and elaborate 
the values of the syntactic features. Table 1 
shows the syntactic features of a syntactic 
pattern with possible values. The syntactic 
features are automatically extracted from the 
corpus using a conventional parser (Kim 
(1994)). 
Manual tagging of speech acts and discourse 
structure information was done by graduate 
students majoring in dialogue analysis and post- 
processed for consistency. The classification of 
speech acts is very subjective without an agreed 
criterion. In this paper, we classified the 17 
types of speech acts that appear in the dialogue 
KS represents the Korean sentence and EN 
represents the translated English sentence. 
231 
corpus. Table 2 shows the distribution of speech 
acts in the tagged dialogue corpus. 
Discourse structures are determined by focusing 
on the subject of current dialogue and are 
hierarchically constructed according to the 
subject. Discourse structure information tagged 
in the corpus is an index that represents the 
hierarchical structure of discourse reflecting the 
depth of the indentation of discourse segments. 
The proposed system transforms this index 
information to discourse segment boundary 
(DSB) information to acquire various statistical 
information. In section 2.2.1, we will describe 
the DSBs in detail. 
Syntactic feature Values 
decl, imperative, 
wh question, yn_question 
Notes 
Sentence T)~e The mood of all utterance 
pvg, pvd, paa, pad, be, The type of the main verb. For 
Main-Verb know, ask, etc. special verbs, lexical items are 
(total 88 kinds) used. 
Tense past, present, future. The tense of an utterance 
Negative Sentence Yes or No Yes if an utterance is negative. 
serve, seem, want, will, The modality of an utterance. Aux-Verb etc. (total 31 kinds) 
Yes, No, OK., etc. The special word used in the 
utterance having particular Clue-Word (total 26 kinds speech acts. 
Table I : Syntactic features used in the syntactic pattern 
Speech Act Type Ratio(%) 
Accept 2.49 
Acknowledge 5.75 
Ask-confirm 3.16 
Ask-if 5.36 
Ask-tel 13.39 
Closing 3.39 
Correct 0.03 
Expressive 5,64 
biform 11.90 
Speech Act Type Ratio(%) 
h~troducing-oneself 6.75 
Offer 0.40 
Opening 6.58 
Promise 2,42 
Reject 1.07 
Request 4.96 
Response 24.73 
Suggest 1.98 
Total 100.00 
Table 2: The distribution of speech acts in corpus 
2 Statistical models 
We construct two statistical models: one for 
speech act analysis and the other for discourse 
structure analysis. We integrate the two models 
using maximum entropy model. In the following 
subsections, we describe these models in detail. 
2.1 Speech act analysis model 
Let UI,, denote a dialogue which consists of a 
sequence of n utterances, U1,U2 ..... U,, and let 
S i denote the speech act of U. With these 
notations, P(SilU1,i) means the probability 
that S~ becomes the speech act of utterance U~ 
given a sequence of utterances U1,U2,...,Ui. 
We can approximate the probability 
P(Si I Ul.i) by the product of the sentential 
probability P(Ui IS i) and the contextual 
probability P( Si I UI, i - i, $1, ~ - 1). Also we can 
approximate P(SilUl, i-l, Si,i-i) by 
P(Si l SI, g -l) (Charniak (1993)). 
P(S~IUI,~)= P(SilS~,~-I)P(U~ISi) (1) 
It has been widely believed that there is a strong 
relation between the speaker's speech act and 
the surface utterances expressing that speech act 
(Hinkelman (1989), Andernach (1996)). That is, 
the speaker utters a sentence, which most well 
expresses his/her intention (speech act) so that 
the hearer can easily infer what the speaker's 
speech act is. The sentential probability 
P(UilSO represents the relationship between 
the speech acts and the features of surface 
sentences. Therefore, we approximate the 
sentential probability using the syntactic pattern 
Pi" 
P(Ui I Si) = P(PiISi) (2) 
The contextual probability P(Si I $1, ~ - 1) is the 
probability that utterance with speech act S i is 
uttered given that utterances with speech act 
$1, $2 ..... S/- 1 were previously uttered. Since it 
is impossible to consider all preceding 
utterances $1, $2 ..... Si - ~ as contextual 
information, we use the n-gram model. 
Generally, dialogues have a hierarchical 
discourse structure. So we approximate the 
context as speech acts of n utterances that are 
hierarchically recent to the utterance. An 
utterance A is hierarchically recent to an 
utterance B if A is adjacent to B in the tree 
structure of the discourse (Walker (1996)). 
Equation (3) represents the approximated 
contextual probability in the case of using 
trigram where Uj and U~ are hierarchically 
recent to the utterance U, where 
l<j<k<i-1. 
232 
P(Si I S\],, - ,) = P(Si I Sj, Sk) (3) 
As a result, the statistical model for speech act 
analysis is represented in equation (4). 
P(S, I U,, 0 = P(Si I S,,, - ,)P(Ui I S,) 
= P(Si IS j, Sk)P(Pi \[St) (4) 
2.2 Discourse structure analysis model 
2.2.1 Discourse segment boundary tagging 
We define a set of discourse segment boundaries 
(DSBs) as the markers for discourse structure 
tagging. A DSB represents the relationship 
between two consecutive utterances in a 
dialogue. Table 3 shows DSBs and their 
meanings, and Figure 3 shows an example of 
DSB tagged dialogue. 
DSB Meaning 
DE Start a new dialogue 
DC Continue a dialogue 
SS Start a sub-dialogue 
nE End n level sub-dialogues 
nB nE and then SS 
Table 3: DSBs and their meanings 
DS DSB 
1) User : I would like to reserve a room. I NULL 
2) Agent : What kind of room do you want? 1.1 SS 
3) User : What kind of room do you have? 1.1.1 SS 
4) Agent : We have single and double rooms. 1.1.1 DC 
5) User : How much are those rooms? 1.!.2 I B 
6) Agent : Single costs 30,000 won and double costs 40,000 won. 1.1.2 DC 
7) User : A single room, please. I. 1 1E 
Figure 3: An example of DSB tagging 
Since the DSB of an utterance represents a 
relationship between the utterance and the 
previous utterance, the DSB of utterance 1 in the 
example dialogue becomes NULL. By 
comparing utterance 2 with utterance 1 in Figure 
3, we know that a new sub-dialogue starts at 
utterance 2. Therefore the DSB of utterance 2 
becomes SS. Similarly, the DSB of utterance 3 
is SS. Since utterance 4 is a response for 
utterance 3, utterance 3 and 4 belong to the same 
discourse segment. So the DSB of utterance 4 
becomes DC. Since a sub-dialogue of one level 
(i.e., the DS 1.1.2) consisting of utterances 3 and 
4 ends, and new sub-dialogue starts at utterance 
5. Therefore, the DSB of utterance 5 becomes 
lB. Finally, utterance 7 is a response for 
utterance 2, i.e., the sub-dialogue consisting of 
utterances 5 and 6 ends and the segment 1.1 is 
resumed. Therefore the DSB of utterance 7 
becomes 1E. 
2.2.2 Statistical model for discourse structure 
analysis 
We construct a statistical model for discourse 
structure analysis using DSBs. In the training 
phase, the model transforms discourse structure 
(DS) information in the corpus into DSBs by 
comparing the DS information of an utterance 
with that of the previous utterance. After 
transformation, we estimate probabilities for 
DSBs. In the analyzing process, the goal of the 
system is simply determining the DSB of a 
current utterance using the probabilities. Now 
we describe the model in detail. 
Let G, denote the DSB of U,. With this notation, 
P(GilU\],O means the probability that G/ 
becomes the DSB of utterance U~ given a 
sequence of utterances U~, U 2 ..... Ui. As shown 
in the equation (5), we can approximate 
P(GilU~,O by the product of the sentential 
probability P(Ui I Gi) and the contextual 
probability P( Gi I U \], i - \]. GI, i - \]) : 
P(GilU1, i) 
= P(Gi I U\], i - \], Gi, i - OP(Ui I Gi) (5) 
In order to analyze discourse structure, we 
consider the speech act of each corresponding 
utterance. Thus we can approximate each 
utterance by the corresponding speech act in the 
sentential probability P(Ui I Gi): 
P(Ui I G0 --- P(SilGO (6) 
233 
Let F, be a pair of the speech act and DSB of U, 
to simplify notations: 
Fi ::- (Si, Gi) (7) 
We can approximate the contextual probability 
P(GilUl.i-i, Gl.i-l) as equation (8) in the 
case of using trigram. 
P(Gi IUl, i-l,Gl, i-1) 
= P(Gi I FI, i - 1) = P(Gi I Fi - 2, Fi - l) (8) 
As a result, the statistical model for the 
discourse structure analysis is represented as 
equation (9). 
P(Gi I UI. i) 
= P(Gi IUl.i-i, Gl.i-OP(Ui IGi) 
= P(G, I F~ - 2, F, - OP(& I GO 
(9) 
2.3 Integrated dialogue analysis model 
Given a dialogue UI,., P(Si, Gi IUl, i) means 
the probability that S~ and G i will be, 
respectively, the speech act and the DSB of an 
utterance U/ given a sequence of utterances 
Ut, U2 ..... U~. By using a chain rule, we can 
rewrite the probability as in equation (10). 
P(Si, Gi I UI, i) 
= P(SiIUI, i)P(GiISi, UI, i) (10) 
In the right hand side (RHS) of equation (10), 
the first term is equal to the speech act analysis 
model shown in section 2.1. The second term 
can be approximated as the discourse structure 
analysis model shown in section 2.2 because the 
discourse structure analysis model is formulated 
by considering utterances and speech acts 
together. Finally the integrated dialogue analysis 
model can be formulated as the product of the 
speech act analysis model and the discourse 
structure analysis model: 
e(Si, Gi I Ul.i) 
= P(S, I ULi)P(Gi I Ul.i) 
= P(S, I Sj, &)P(P, I SO 
x P(G~ I Fi - 2, F~ - OP(Si I GO 
(10 
2.4 Maximum entropy model 
All terms in RHS of equation (11) are 
represented by conditional probabilities. We 
estimate the probability of each term using the 
following representative equation: 
P(a lb)= P(a,b) 
y~ P(a', b) 
a 
(12) 
We can evaluate P(a,b) using maximum 
entropy model shown in equation (13) (Reynar 
1997). 
P(a,b) = lrI" I Ot\[ '(''b) 
i=1 
where 0 < c~ i < oo, i = { 1,2 ..... k } 
(13) 
In equation (13), a is either a speech act or a 
DSB depending on the term, b is the context (or 
history) of a, 7r is a normalization constant, and 
is the model parameter corresponding to each 
feature functionf. 
In this paper, we use two feature functions: 
unified feature function and separated feature 
function. The former uses the whole context b as 
shown in equation (12), and the latter uses 
partial context split-up from the whole context 
to cope with data sparseness problems. Equation 
(14) and (15) show examples of these feature 
functions for estimating the sentential 
probability of the speech act analysis model. 
iff a = response and (14) 
b = User : \[decl, pvd, future, no, will, then\] 
otherwise 
10 iff a = response and 
f(a,b) = SentenceType(b) = User : decl 
otherwise 
(15) 
Equation (14) represents a unified feature 
function constructed with a syntactic pattern 
234 
having all syntactic features, and equation (15) 
represents a separated feature function 
constructed with only one feature, named 
Sentence Type, among all syntactic features in 
the pattern. The interpretation of the unified 
feature function shown in equation (14) is that if 
the current utterance is uttered by "User", the 
syntactic pattern of the utterance is 
\[decl,pvd,future,no,will,then\] and the speech act 
of the current utterance is response then f(a,b)= 1 
else f(a,b)=O. We can construct five more 
separated feature functions using the other 
syntactic features. The feature functions for the 
contextual probability can be constructed in 
similar ways as the sentential probability. Those 
are unified feature functions with feature 
trigrams and separated feature functions with 
distance-1 bigrams and distance-2 bigrams. 
Equation (16) shows an example of an unified 
feature function, and equation (17) and (18) 
which are delivered by separating the condition 
of b in equation (16) show examples of 
separated feature functions for the contextual 
probability of the speech act analysis model. 
10 iff a = response and 
f(a, b) = b = User : request, Agent : ask - ref 
otherwise 
where b is the information of Ujand Uk 
defined in equation (3) 
(16) 
10 iff a = response and 
f(a,b) = b_ t = Agent : ask - ref 
otherwise 
where b_~ is the information of Uk defined in equation (3) 
(17) 
f(a'b)={lo iffa=resp°nseandb-2otherwise=USer:request 
where b_ 2 is the information of Ujdefined in equation (3) 
(18) 
Similarly, we can construct feature functions for 
the discourse structure analysis model. For the 
sentential probability of the discourse structure 
analysis model, the unified feature function is 
identical to the separated feature function since 
the whole context includes only a speech act. 
Using the separated feature functions, we can 
solve the data sparseness problem when there 
are not enough training examples to which the 
unified feature function is applicable. 
3 Experiments and results 
In order to experiment the proposed model, we 
used the tagged corpus shown in section 1. The 
corpus is divided into the training corpus with 
428 dialogues, 8,349 utterances (19.51 
utterances per dialogue), and the testing corpus 
with 100 dialogues, 1,936 utterances (19.36 
utterances per dialogue). Using the Maximum 
Entropy Modeling Toolkit (Ristad 1996), we 
estimated the model parameter ~ corresponding 
to each feature functionf in equation (13). 
We made experiments with two models for each 
analysis model. Modem uses only the unified 
feature function, and Model-II uses the unified 
feature function and the separated feature 
function together. Among the ways to combine 
the unified feature function with the separated 
feature function, we choose the combination in 
which the separated feature function is used only 
when there is no training example applicable for 
the unified feature function. 
First, we tested the speech act analysis model 
and the discourse analysis model. Table 4 and 5 
show the results for each analysis model. The 
results shown in table 4 are obtained by using 
the correct structural information of discourse, 
i.e., DSB, as marked in the tagged corpus. 
Similarly those in table 5 are obtained by using 
the correct speech act information from the 
tagged corpus. 
Accuracy (Closed test) Accuracy (Open test) 
Candidates Top-1 Top-3 Top-1 Top-3 
Lee (1997) 78.59% 97.88% 
Samuel (1998) 73.17% 
Reithinger (1997) 74.70% 
Model I 90.65% 99.66% 81.61% 93.18% 
Model II 90.65% 99.66% 83,37% 95.35% 
Table 4. Results of speech act analysis 
Accuracy(Open test) 
Candidates Top-I Top-3 
Model I 81.51% 98.55% 
Model I\] 83.21% 99.02% 
Table 5, Results of discourse structure analysis 
In the closed test in table 4, the results of Model- 
I and Model-II are the same since the 
probabilities of the unified feature functions 
always exist in this case. As shown in table 4, 
the proposed models show better results than 
previous work, Lee (1997). As shown in table 4 
and 5, ModeMI shows better results than Model- 
235 
I in all cases. We believe that the separated 
feature functions are effective for the data 
sparseness problem. In the open test in table 4, it 
is difficult to compare the proposed model 
directly with the previous works like Samuel 
(1998) and Reithinger (1997) because test data 
used in those works consists of English 
dialogues while we use Korean dialogues. 
Furthermore the speech acts used in the 
experiments are different. We will test our 
model using the same data with the same speech 
acts as used in those works in the future work. 
We tested the integrated dialogue analysis model 
in which speech act and discourse structure 
analysis models are integrated. The integrated 
model uses ModeMI for each analysis model 
because it showed better performance. In this 
model, after the system determing the speech act 
and DSB of an utterance, it uses the results to 
process the next utterance, recursively. The 
experimental results are shown in table 6. 
As shown in table 6, the results of the integrated 
model are worse than the results of each analysis 
model. For top-1 candidate, the performance of 
the speech act analysis fell off about 2.89% and 
that of the discourse structure analysis about 
7.07%. Nevertheless, the integrated model still 
shows better performance than previous work in 
the speech act analysis. 
Accuracy(Open test) 
Candidates Top- 1 Top-3 
Result of speech act 80.48% 94.58% 
analysis 
Result of discourse 76.14% 95.45% 
structure analysis 
Table 6. Results of the integrated anal, 'sis model 
Conclusion 
In this paper, we propose a statistical dialogue 
analysis model which can perform both speech 
act analysis and discourse structure analysis 
using maximum entropy model. The model can 
automatically acquire discourse knowledge from 
a discourse tagged corpus to resolve ambiguities. 
We defined the DSBs to represent the structural 
relationship of discourse between two 
consecutive utterances in a dialogue and used 
them for statistically analyzing both the speech 
act of an utterance and the discourse structure of 
a dialogue. By using the separated feature 
functions together with the unified feature 
functions, we could alleviate the data sparseness 
problems to improve the system performance. 
The model can, we believe, analyze dialogues 
more effectively than other previous works 
because it manages speech act analysis and 
discourse structure analysis at the same time 
using the same framework. 
Acknowledgements 
Authors are grateful to the anonymous reviewer 
for their valuable comments on this paper. 
Without their comments, we may miss important 
mistakes made in the original draft. 

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