Utilizing Statistical Dialogue Act Processing in Verbmobil 
Norbert Reithinger and Elisabeth Maier* 
DFKI GmbH 
Stuhlsatzenhausweg 3 
D-66123 Saarbriicken 
Germany 
{re±thinger, maier}@dfki, uni- sb. de 
Abstract 
In this paper, we present a statistical ap- 
proach for dialogue act processing in the di- 
alogue component of the speech-to-speech 
translation system VERBMOBIL. Statistics 
in dialogue processing is used to predict 
follow-up dialogue acts. As an application 
example we show how it supports repair 
when unexpected dialogue states occur. 
1 Introduction 
Extracting and processing communicative intentions 
behind natural language utterances plays an im- 
portant role in natural language systems (see e.g. 
(Cohen et al., 1990; Hinkelman and Spackman, 
1994)). Within the speech-to-speech translation sys- 
tem VERBMOBIL (Wahlster, 1993; Kay et al., 1994), 
dialogue acts are used as the basis for the treatment 
of intentions in dialogues. The representation of in- 
tentions in the VERBMOBIL system serves two main 
purposes: 
• Utilizing the dialogue act of an utterance as 
an important knowledge source for transla- 
tion yields a faster and often qualitative better 
translation than a method that depends on sur- 
face expressions only. This is the case especially 
in the first application of VV.RBMOBIL, the on- 
demand translation of appointment scheduling 
dialogues. 
• Another use of dialogue act processing in VERB- 
MOBIL is the prediction of follow-up dialogue 
acts to narrow down the search space on the 
analysis side. For example, dialogue act pre- 
dictions are employed to allow for dynamically 
adaptable language models in word recognition. 
*This work was funded by the German Federal Min- 
istry for Education, Research and Technology (BMBF) 
in the framework of the Verbmohil Project under Grant 
01IV101K/1. The responsibility for the contents of this 
study lies with the authors. Thanks to Jan Alexanders- 
son for valuable comments and suggestions on earlier 
drafts of this paper. 
Recent results (e.g. (Niedermair, 1992)) show a 
reduction of perplexity in the word recognizer 
between 19% and 60% when context dependent 
language models are used. 
DiMogue act determination in VERBMOBIL is done 
in two ways, depending on the system mode: using 
deep or shallow processing. These two modes depend 
on the fact that VERBMOBIL is only translating on 
demand, i.e. when the user's knowledge of English 
is not sufficient to participate in a dialogue. If the 
user of VERBMOBIL needs translation, she presses a 
button thereby activating deep processing. In depth 
processing of an utterance takes place in maximally 
50% of the dialogue contributions, namely when the 
owner speaks German only. DiMogue act extraction 
from a DRS-based semantic representation (Bos et 
al., 1994) is only possible in this mode and is the 
task of the semantic evaluation component of VERB- 
MOBIL. 
In the other processing mode the diMogue com- 
ponent tries to process the English passages of the 
diMogue by using a keyword spotter that tracks the 
ongoing dialogue superficiMly. Since the keyword 
spotter only works reliably for a vocabulary of some 
ten words, it has to be provided with keywords which 
typically occur in utterances of the same diMogue 
act type; for every utterance the dialogue component 
supplies the keyword spotter with a prediction of the 
most likely follow-up dialogue act and the situation- 
dependent keywords. 
The dialogue component uses a combination of 
statistical and knowledge based approaches to pro- 
cess dialogue acts and to maintain and to provide 
contextual information for the other modules of 
VERBMOBIL (Maier and McGlashan, 1994). It in- 
cludes a robust dialogue plan recognizing module, 
which uses repair techniques to treat unexpected di- 
alogue steps. The information acquired during di- 
alogue processing is stored in a dialogue memory. 
This contextual information is decomposed into the 
intentional structure, the referential structure, and 
the temporal structure which refers to the dates 
mentioned in the dialogue. 
116 
An overview of the dialogue component is given 
in (Alexandersson et al., 1995). In this paper main 
emphasis is on statistical dialogue act prediction in 
VEFtBMOBIL, with an evaluation of the method, and 
an example of the interaction between plan recogni- 
tion and statistical dialogue act prediction. 
Main Wadoguo 
Gro~ Suggea 
Introduce Init I=lequoet_CornmQmt 
Requut Commont 
• Commont / 
Thank Su99eet Requeet_Comment Con(mrn 
PotonUol additions 
In any cllelogue 
Clarily_Amvo¢ °--.-= <, / 
I:)igam= V COa~y_Ou=ry 
I 1-1 Initial Stw 0 Final State • Nc~4iaal SUm \[ 
Figure 1: A dialogue model for the description of 
appointment scheduling dialogs 
2 The Dialogue Model and 
Predictions of Dialogue Acts 
Like previous approaches for modeling task-oriented 
dialogues we assume that a dialogue can be de- 
scribed by means of a limited but open set of di- 
alogue acts (see e.g. (Bilange, 1991), (Mast et al., 
1992)). We selected the dialogue acts by examining 
the VERBMOBIL corpus, which consists of transliter- 
ated spoken dialogues (German and English) for ap- 
pointment scheduling. We examined this corpus for 
the occurrence of dialogue acts as proposed by e.g. 
(Austin, 1962; Searle, 1969) and for the necessity to 
introduce new, sometimes problem-oriented dialogue 
acts. We first defined 17 dialogue acts together with 
semi-formal rules for their assignment to utterances 
(Maier, 1994). After one year of experience with 
these acts, the users of dialogue acts in VERBMOBIL 
selected them as the domain independent "upper" 
concepts within a more elaborate hierarchy that be- 
comes more and more propositional and domain de- 
pendent towards its leaves (Jekat et al., 1995). Such 
a hierarchy is useful e.g. for translation purposes. 
Following the assignment rules, which also served 
as starting point for the automatic determination of 
dialogue acts within the semantic evaluation com- 
ponent, we hand-annotated over 200 dialogues with 
dialogue act information to make this information 
available for training and test purposes. 
Figure 1 shows the domain independent dialogue 
acts and the transition networks which define admis- 
sible sequences of dialogue acts. In addition to the 
dialogue acts in the main dialogue network, there are 
five dialogue acts, which we call deviations, that can 
occur at any point of the dialogue. They are repre- 
sented in an additional subnetwork which is shown 
at the bottom of figure 1. The networks serve as 
the basis for the implementation of a parser which 
determines whether an incoming dialogue act is com- 
patible with the dialogue model. 
As mentioned in the introduction, it is not only 
important to extract the dialogue act of the cur- 
rent utterance, but also to predict possible follow 
up dialogue acts. Predictions about what comes 
next are needed internally in the dialogue compo- 
nent and externally by other components in VERB- 
MOBIL. An example of the internal use, namely the 
treatment of unexpected input by the plan recog- 
nizer, is described in section 4. Outside the dialogue 
component dialogue act predictions are used e.g. by 
the abovementioned semantic evaluation component 
and the keyword spotter. The semantic evaluation 
component needs predictions when it determines the 
dialogue act of a new utterance to narrow down the 
set of possibilities. The keyword spotter can only 
detect a small number of keywords that are selected 
for each dialogue act from the VERBMOBIL corpus of 
annotated dialogues using the Keyword Classifica- 
tion Tree algorithm (Kuhn, 1993; Mast, 1995). 
For the task of dialogue act prediction a knowledge 
source like the network model cannot be used since 
the average number of predictions in any state of the 
main network is five. This number increases when 
the five dialogue acts from the subnetwork which can 
occur everywhere are considered as well. In that case 
the average number of predictions goes up to 10. Be- 
cause the prediction of 10 dialogue acts from a total 
number of 17 is not sufficiently restrictive and be- 
cause the dialogue network does not represent pref- 
erence information for the various dialogue acts we 
need a different model which is able to make reliable 
dialogue act predictions. Therefore we developed a 
statistical method which is described in detail in the 
next section. 
3 The Statistical Prediction Method 
and its Evaluation 
In order to compute weighted dialogue act predic- 
tions we evaluated two methods: The first method 
is to attribute probabilities to the arcs of our net- 
work by training it with annotated dialogues from 
our corpus. The second method adopted informa- 
tion theoretic methods from speech recognition. We 
117 
implemented and tested both methods and currently 
favor the second one because it is insensitive to de- 
viations from the dialogue structure as described by 
the dialogue model and generally yields better pre- 
diction rates. This second method and its evaluation 
will be described in detail in this section. 
Currently, we use n-gram dialogue act probabil- 
ities to compute the most likely follow-up dialogue 
act. The method is adapted from speech recogni- 
tion, where language models are commonly used to 
reduce the search space when determining a word 
that can match a part of the input signal (Jellinek, 
1990). It was used for the task of dialogue act pre- 
diction by e.g. (Niedermair, 1992) and (Nagata and 
Morimoto, 1993). For our purpose, we consider a di- 
alogue S as a sequence of utterances Si where each 
utterance has a corresponding dialogue act si. If 
P(S) is the statistical model of S, the probability 
can be approximated by the n-gram probabilities 
P(S) = H P(siIsi-N+I'"" S,-l) 
i=1 
Therefore, to predict the nth dialogue act sn we 
can use the previously uttered dialogue acts and de- 
termine the most probable dialogue act by comput- 
ing 
s. := max P(sls._;, s,,-u, s.,-z, ...) 
$ 
To approximate the conditional probability P(.I.) 
the standard smoothing technique known as deleted 
interpolation is used (Jellinek, 1990) with 
P(s.ls.-,,s.-2) = 
qlf(sn) q- qzf(sn Is.-x) + q3f(Sn I'.-1, s.-u) 
where f are the relative frequencies computed 
from a training corpus and qi weighting factors with 
~"~qi = 1. 
To evaluate the statistical model, we made vari- 
ous experiments. Figure 2 shows the results for three 
representative experiments (TS1-TS3, see also (Rei- 
thinger, 1995)). 
I Pred. I TS1 TS2 TS3 
1 44,24% 37.47 % 40.28% 
2 66,47 % 56.50% 59.62% 
3 81,46% 69.52% 71.93% 
Figure 2: Predictions and hit rates 
In all experiments 41 German dialogues (with 
2472 dialogue acts) from our corpus are used as 
training data, including deviations. TS1 and TS2 
use the same 81 German dialogues as test data. The 
difference between the two experiments is that in 
TS1 only dialogue acts of the main dialogue network 
are processed during the test, i.e. the deviation acts 
of the test dialogues are not processed. As can be 
seen -- and as could be expected -- the prediction 
rate drops heavily when unforseeable deviations oc- 
cur. TS3 shows the prediction rates, when all cur- 
rently available annotated dialogues (with 7197 dia- 
logue acts) from the corpus are processed, including 
deviations. 
16 
w 
m 
w 
M 
m | | | $ Io 
I$ 
| ! ! | i ! 
Figure 3: Hit rates for 47 dialogues using 3 predic- 
tions 
Compared to the data from (Nagata and Mori- 
moto, 1993) who report prediction rates of 61.7 %, 
77.5% and 85.1% for one, two or three predictions 
respectively, the predictions are less reliable. How- 
ever, their set of dialogue acts (or the equivalents, 
called illocutionary force types) does not include di- 
alogue acts to handle deviations. Also, since the 
dialogues in our corpus are rather unrestricted, they 
have a big variation in their structure. Figure 3 
shows the variation in prediction rates of three dia- 
logue acts for 47 dialogues which were taken at ran- 
dom from our corpus. The x-axis represents the dif- 
ferent diMogues, while the y-axis gives the hit rate 
for three predictions. Good examples for the differ- 
ences in the dialogue structure are the diMogue pairs 
#15/#16 and #41/#42. The hit rate for dialogue 
#15 is about 54% while for #16 it is about 86%. 
Even more extreme is the second pair with hit rates 
of approximately 93% vs. 53%. While diMogue #41 
fits very well in the statisticM model acquired from 
the training-corpus, dialogue #42 does not. This 
figure gives a rather good impression of the wide va- 
riety of material the dialogue component has to cope 
with. 
4 Application of the Statistical 
Model: Treatment of Unexpected 
Input 
The dialogue model specified in the networks mod- 
els all diMogue act sequences that can be usually 
expected in an appointment scheduling dialogue. In 
case unexpected input occurs repair techniques have 
118 
to be provided to recover from such a state and to 
continue processing the dialogue in the best possible 
way. The treatment of these cases is the task of the 
dialogue plan recognizer of the dialogue component. 
The plan recognizer uses a hierarchical depth-first 
left-to-right technique for dialogue act processing 
(Vilain, 1990). Plan operators have been used to 
encode both the dialogue model and methods for re- 
covery from erroneous dialogue states. Each plan 
operator represents a specific goal which it is able 
to fulfill in case specific constraints hold. These 
constraints mostly address the context, but they 
can also be used to check pragmatic features, like 
e.g. whether the dialogue participants know each 
other. Also, every plan operator can trigger follow- 
up actions, h typical action is, for example, the 
update of the dialogue memory. To be able to fulfill 
a goal a plan operator can define subgoals which 
have to be achieved in a pre-specified order (see e.g. 
(Maybury, 1991; Moore, 1994) for comparable ap- 
proaches). 
fmwl_2_01: der Termin den wir neulich 
abgesprochen haben am zehnten an dem 
Samstag (MOTIVATE) 
(the date we recently agreed upon, the lOth that 
Saturday) 
da kann ich doch nich' (REJECT) 
(then I can not) 
wit sollten einen anderen ausmachen (INIT) 
(we should make another one) 
mpsl_2_02: wean ich da so meinen Termin- 
Kalender anschaue, (DELIBERATE) 
(if I look at my diary) 
dan sieht schlecht aus (REJECT). 
(that looks bad) 
Figure 4: Part of an example dialogue 
Since the VERBMOBIL system is not actively par- 
ticipating in the appointment scheduling task but 
only mediating between two dialogue participants it 
has to be assumed that every utterance, even if it 
is not consistent with the dialogue model, is a legal 
dialogue step. The first strategy for error recovery 
therefore is based on the hypothesis that the attri- 
bution of a dialogue act to a given utterance has 
been incorrect or rather that an utterance has vari- 
ous facets, i.e. multiple dialogue act interpretations. 
Currently, only the most plausible dialogue act is 
provided by the semantic evaluation component. To 
find out whether there might be an additional inter- 
pretation the plan recognizer relies on information 
provided by the statistics module. If an incompat- 
ible dialogue act is encountered, an alternative dia- 
logue act is looked up in the statistical module which 
is most likely to come after the preceding dialogue 
act and which can be consistently followed by the 
current dialogue act, thereby gaining an admissible 
dialogue act sequence. 
To illustrate this principle we show a part of the 
processing of two turns (fmwl..2_01 and mpsl_2_02, 
see figure 4) from an example dialogue with the di- 
alogue act assignments as provided by the seman- 
tic evaluation component. The translations stick to 
the German words as close as possible and are not 
provided by VERBMOBIL. The trace of the dialogue 
component is given in figure 5, starting with pro- 
cessing of INIT. 
Planner: -- Processing INIT 
Planner: -- Processing DELIBERATE 
Warning -- Repairing... 
Planner: -- Processing REJECT 
Trying to find a dialogue act to bridge 
DELIBERATE and REJECT ... 
Possible insertions and their scores: 
((SUGGEST 81326) 
(REQUEST_COMMENT 37576) 
(DELIBERATE20572)) 
Testing SUGGEST for compatibility with 
surrounding dialogue acts... 
The previomsdialogue act INIT 
has an additional reading of SUGGEST: 
INIT -> INIT SUGGEST ! 
Warning -- Repairing... 
Planner: -- Processing IiIT 
Planner: -- Processing SUGGEST 
,.. 
Figure 5: Example of statistical repair 
In this example the case for statistical repair oc- 
curs when a REJECT does not - as expected - follow 
a SUGGEST. Instead, it comes after the INIT of the 
topic to be negotiated and after a DELIBERATE. The 
latter dialogue act can occur at any point of the 
dialogue; it refers to utterances which do not con- 
tribute to the negotiation as such and which can be 
best seen as "thinking aloud". As first option, the 
plan recognizer tries to repair this state using sta- 
tistical information, finding a dialogue act which is 
able to connect INIT and REJECT 1. As can be seen in 
figure 5 the dialogue acts REQUEST_COMMENT, DE- 
LIBERATE, and SUGGEST can be inserted to achieve 
a consistent dialogue. The annotated scores are the 
product of the transition probabilities times 1000 be- 
tween the previous dialogue act, the potential inser- 
tion and the current dialogue act which are provided 
1 Because DELIBERATE has only the function of "so- 
cial noise" it can be omitted from the following 
considerations. 
119 
by the statistic module. Ordered according to their 
scores, these candidates for insertion are tested for 
compatibility with either the previous or the current 
dialogue act. The notion of compatibility refers to 
dialogue acts which have closely related meanings or 
which can be easily realized in one utterance. 
To find out which dialogue acts can be combined 
we examined the corpus for cases where the repair 
mechanism proposes an additional reading. Looking 
at the sample dialogues we then checked which of the 
proposed dialogue acts could actually occur together 
in one utterance, thereby gaining a list of admissi- 
ble dialogue act combinations. In the VERBMOBIL 
corpus we found that dialogue act combinations like 
SUGGEST and REJECT can never be attributed to one 
utterance, while INIT can often also be interpreted 
as a SUQGEST therefore getting a typical follow-up 
reaction of either an acceptance or a rejection. The 
latter case can be found in our example: INIT gets 
an additional reading of SUGeEST. 
In cases where no statistical solution is possible 
plan-based repair is used. When an unexpected di- 
alogue act occurs a plan operator is activated which 
distinguishes various types of repair. Depending on 
the type of the incoming dialogue act specialized 
repair operators are used. The simplest case cov- 
ers dialogue acts which can appear at any point of 
the dialogue, as e.g. DELIBERATE and clarification 
dialogues (CLARIFY_QUERY and CLARIFY-ANSWER). 
We handle these dialogue acts by means of repair in 
order to make the planning process more efficient: 
since these dialogue acts can occur at any point in 
the dialogue the plan recognizer in the worst case 
has to test for every new utterance whether it is one 
of the dialogue acts which indicates a deviation. To 
prevent this, the occurrence of one of these dialogue 
acts is treated as an unforeseen event which triggers 
the repair operator. In figure 5, the plan recognizer 
issues a warning after processing the DELIBERATE di- 
alogue act, because this act was inserted by means 
of a repair operator into the dialogue structure. 
5 Conclusion 
This paper presents the method for statistical dia- 
logue act prediction currently used in the dialogue 
component of VERBMOBIL. It presents plan repair 
as one example of its use. 
The analysis of the statistical method shows that 
the prediction algorithm shows satisfactory results 
when deviations from the main dialogue model are 
excluded. If dialogue acts for deviations are in- 
cluded, the prediction rate drops around 10%. The 
analysis of the hit rate shows also a large variation 
in the structure of the dialogues from the corpus. 
We currently integrate the speaker direction into the 
prediction process which results in a gain of up to 
5 % in the prediction hit rate. Additionally, we in- 
vestigate methods to cluster training dialogues in 
classes with a similar structure. 
An important application of the statistical predic- 
tion is the repair mechanism of the dialogue plan rec- 
ognizer. The mechanism proposed here contributes 
to the robustness of the whole VERBMOBIL system 
insofar as it is able to recognize cases where dialogue 
act attribution has delivered incorrect or insufficient 
results. This is especially important because the in- 
put given to the dialogue component is unreliable 
when dialogue act information is computed via the 
keyword spotter. Additional dialogue act readings 
can be proposed and the dialogue history can be 
changed accordingly. 
Currently, the dialogue component processes more 
than 200 annotated dialogues from the VERBMOBIL 
corpus. For each of these dialogues, the plan rec- 
ognizer builds a dialogue tree structure, using the 
method presented in section 4, even if the dialogue 
structure is inconsistent with the dialogue model. 
Therefore, our model provides robust techniques for 
the processing of even highly unexpected dialogue 
contributions. 
In a next version of the system it is envisaged that 
the semantic evaluation component and the keyword 
spotter are able to attribute a set of dialogue acts 
with their respective probabilities to an utterance. 
Also, the plan operators will be augmented with sta- 
tistical information so that the selection of the best 
possible follow-up dialogue acts can be retrieved by 
using additional information from the plan recog- 
nizer itself. 

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