Proceedings of EACL '99 
Dialogue Processing in a CALL-System 
Veit Reuer 
Institut fiir deutsche Sprache und Linguistik 
Humboldt-Universitgt zu Berlin 
Unter den Linden 6 
10099 Berlin 
GERMANY 
Veit.Reuer@compling.hu-berlin.de 
Abstract 
In a CALL-environment (Computer- 
assisted language learning) programs 
should ideally allow the learner to 
train his/her communicative competence, 
which is one of the main goals of foreign 
language teaching nowadays. This can 
be reached by allowing learners to use 
and train their knowledge of a foreign 
language in realistic dialogue-style exer- 
cises. All levels of linguistic and com- 
municational analysis have to be consid- 
ered to realize such a system. In this 
paper I will concentrate on the dialogue 
component of the concept, which relies 
on two main knowledge sources. The 
discourse grammar structures the dia- 
logue elements (or dialogue acts) as pos- 
sible parts of a dialogue and the dialogue 
knowledge base provides the possible con- 
tents of dialogues. Additionally, a fram- 
ing discourse structure has to be built to 
provide the specific dialogue-exercise. A 
FSA (finite state automaton) based on 
the discourse grammar determines the 
possible moves which the dialogue might 
take. On the one hand this concept is re- 
stricted enough to allow for (relatively) 
easy maintenance as well as expansion 
and on the other hand it is advanced 
enough to allow for simulated complex 
dialogues. 
1 Introduction 
Today the main goal in foreign language teaching 
is acquiring the so-called communicative compe- 
tence instead of only memorizing the structure of 
the language. This can be achieved by making 
active language production one of the main parts 
of the curriculum. Being an efficient part of the 
media to enhance the learning process, computers 
should present tasks that support the acquisition 
of communicative competence. In the present con- 
cept, a presentation of situations is suggested, in 
which the learner has to produce language, i.e. 
produce complete sentences in a simulated dia- 
logue. Various program modules analyse the in- 
put linguistically, give feedback in case of errors 
and present appropriate reactions to continue the 
dialogue. This gives language learners a chance 
to use their knowledge of the second language in 
a meaningful situation apart from the class room 
setting. 
Three goals are of relevance: 1) the language 
learner should be encouraged to enter free-formed 
input instead of thinking about the 'expectation' 
of the program; 2) the program should offer re- 
liable feedback to the learner about his/her per- 
formance and 3) the program should be (easily) 
expandable. 
When one uses the program, a situation is pre- 
sented to the learner in which s/he is required to 
act in order to solve the particular problem at 
hand. For example, the learner is asked to buy 
tickets for a movie and has to engage in a writ- 
ten dialogue with the computer as the seller of the 
tickets. 
The motivation for the development of the sys- 
tem arises from the above mentioned pedagogi- 
cal considerations and the insight that traditional 
language learning programs do offer only few or 
none of the features to reach the above mentioned 
goals. The main part of this paper, however, deals 
only with the computational aspects of the prob- 
lem: a possible way to implement such a dialogue 
component. In the next section I will focus on 
the dialogue component, which on the one hand 
allows the learner to communicate with the com- 
puter in various dialogue situations, and on the 
other hand is restricted enough to be easily ex- 
pandable and maintainable and gives the possibil- 
ity for advanced feedback. Finally I will give a 
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Proceedings of EACL '99 
rough sketch of the complete system. 
2 Discourse Grammars 
One way of realizing such dialogues is to de- 
velop discourse grammars which describe the steps 
through distinct parts of dialogues. This possibil- 
ity is chosen in the present concept, since it en- 
ables the learner to lead written, situation-based 
dialogues almost as in the class-room situation. 
The advantage of a discourse grammar over a com- 
pletely plan-based dialogue structure is the sepe- 
rate representation of possible moves ('dialogue 
acts' in Alexandersson et al. (1994)) and the con- 
tent of the discourse. The discourse grammar of 
an 'information-gathering' dialogue can be used 
while reporting an accident as well as while or- 
dering a pizza. In the first case the police officer 
wants to know all about the accident and possible 
casualties and in the latter case the pizza deliv- 
ery wants to know the toppings and size of the 
pizza. On the other hand guidance is needed for 
the learner in the CALL-scenario. Systems like 
the one described in Carberry (1990) are much 
too open to be used for language learning. The 
system would not be able to give any feedback to 
the learner in case of erroneous input. Therefore 
the system uses only restricted knowledge about 
what types of input to expect and how to react 
to them since the general intentions of the learner 
are known to the system through the situation 
presented to the learner. In other NLP-based sys- 
tems like 'Herr Kommissar' (deSmedt, 1995) and 
'LINGO' (Murray, 1995), the dialogue with the 
system either allows only single question-answer 
exchanges or is strongly embedded into the re- 
spective scenario. In the first case the structure 
of a complete dialogue does not become clear to 
the learner and the initiative is with the learner 
who might not know what to do. In the second 
case it is difficult to include new scenarios since 
not only the content of the new dialogue has to 
be coded but also the various dialogue structures. 
Moreover the design of a system might not allow 
for different types of dialogues: 
The dialogue component contains two main 
knowledge bases: The first one contains the dis- 
course grammars, which structure so-called 'goal- 
driven dialogues' or 'task-oriented dialogues'. I 
The idea of discourse grammars as a means to han- 
dle dialogue situations is for instance presented in 
Fawcett and Taylor (1989). The second knowledge 
base contains knowledge about the content of the 
dialogue itself. This data is used to infer a mean- 
1 For a discussion about discourse grammars in gen- 
eral see e.g. Taylor et al. (1989). 
ingful reaction to the input sentence. Additionally 
this base contains slots in which the information 
given by the learner is stored. 
The following figure shows a simplified part of a 
discourse grammar, which models an information 
gathering dialogue such as is necessary in the case 
of collecting information about an accident. Ad- 
ditional items of discourse grammars are of course 
needed, for example, to start and end a telephone 
call, etc. 
The same type of structures is also used in 
the analysis of dialogues, e.g. (Carletta et al., 
1997). Here dialogues are analysed with the help 
of a 'Dialogue Structure Coding Scheme', which in 
particular contains only a limited number of pos- 
sible moves between dialogue partners. A similar 
analysis was done in the preparational phase of the 
Verbmobil project (Alexandersson et al., 1994). In 
a dialogue system where the intentions of the dia- 
logue partners are known and the fixed structures 
serve to assess the performance of the language 
learner, the restrictions will probably not make 
the overall behaviour of the system worse than 
more flexible dialogue systems. 
in f o_gather 
QUESTION open 
questions / ~ 
interpretable uninterpretable 
answer answer 
CONFIRMs_.__. interpretable _____- INQUIRE answer / 
no more uninterpretable 
questions answer 
I THANK-ENDI 
Figure 1: Simplified discourse grammar 
The dialogue module uses a surrounding dis- 
course grammar, which includes the grammar 
parts for starting and ending a telephone call etc. 
From here the information gathering structure is 
called to try to fill the variables in the dialogue 
knowledge base (see below) by asking the learner 
a question. This process is continued as long as 
there are open questions (open questions) or un- 
til the learner does not provide interpretable in- 
put even after a repeated question (INQUIRE - 
THANK.END). 
The dialogue knowledge base contains the data 
254 
Proceedings of EACL '99 
necessary to lead a dialogue with a certain con- 
tent. The data is organized in a hierarchical struc- 
ture. In the 'police call' example the root-node 
consists of a slot with a first reaction of the offi- 
cer (greeting) to be presented to the learner. The 
daughter nodes (e.g. accident, theft) contain some 
slots which are used for the actual presentation of 
reactions on the screen or for information storage 
and retrieval. Some slots are: 
- question for pieces of information: This 
includes canned text, which is pre- 
sented to the learner. For example 
the police officer might ask 'Are there 
any injured people?'. 
- information about expected answer: 
The semantic structure of the 
learner's input is checked against the 
content of this slot and in case of 
variables it is stored. 
- keywords to match the learner's input: 
In case the parser was not able to pro- 
duce a semantic representation, the 
system retreats to keyword matching 
in order to provide at least some re- 
action. 
- text as answer: A sentence is passed to 
the learner to acknowledge or confirm 
the processing of the input ('So, there 
has been an accident.'}. 
In case the system chooses to ask a question 
based on the discourse grammar, the question 
from the appropriate slot in a daughter node (top- 
down left-right) is passed to the learner. After 
the grammatical processing of the answer, the 
content is checked against the expected one. If 
they match, a confirmation may be passed to the 
learner and the next step in the discourse gram- 
mar is taken. If the answer was considered not 
appropriate for the question the system tries to 
find a response in a hierarchy of steps from world 
knowledge checking to simple keyword analysis. 
The final output can thus be from the same node, 
a subnode or from a more general independent 
source of possible reactions. Some mechanism 
has to manage the matching-procedure of the sen- 
tence. Possible mechanisms thus include: 
- the content matches completely: The 
system was able to recognize the in- 
put sentence as some meaningful re- 
action to the previous question or 
statement. 
- the content fits only partly (too gen- 
eral): There are subnodes which in- 
elude variables for more specific in- 
formation. 
- the content fits only partly (only one 
aspect}: A general keyword-based 
mechanism recognizes only parts of 
the expected input. If possible the 
learner is asked for further clarifica- 
tion. 
the content does not fit: A ques- 
tion for rephrasal will be displayed to 
the learner. Additionally the learner 
might consult a helpfile with informa- 
tion about how to proceed in the cur- 
rent situation. 
A difficulty that might arise is the change of 
control (or initiative) between the dialogue part- 
nets. Allowing the learner to take the initiative 
has several consequences, which are difficult to 
realize. In contrast to the present concept the 
dialogue module should include a language gener- 
ation device to generate natural language output 
to database-inquiries. From this follows that the 
dialogue knowledge base should not contain any 
contradictions etc. to allow for easy inference of 
possible answers to the input question. Finally 
in case the learner keeps on asking questions the 
system might fail to continue the dialogue in a 
meaningful way. Thus a system designed for the 
use by pupils must be rigid enough to deal with 
this kind of input. 
The seemingly limited flexibility in this system 
is not really a disadvantage, because 1) the learner 
is suppose d to act in a foreseeable way and 2) the 
system should give feedback in case of deviating 
action. Especially the latter seems only possible if 
a discourse grammar structures the moves which 
dialogue partners might take. 
3 System Overview 
The idea behind the system is to extend the types 
of training which the student gets in a class room 
setting into a computer. One important kind of 
training is the practising of dialogues. Therefore 
the program realizes small written dialogues for 
the learner to train her/his 'communicative com- 
petence', as explained above. 
The system consists of four main modules. The 
dialogue control module mainly functions as an in- 
terface. It organizes the flow of the input data be- 
tween the user-interface and the various process- 
ing modules. Every input sentence is first passed 
to the linguistic module, which checks it for ortho- 
graphic and syntactic errors. The orthographic 
check is done in the spirit of Oflazer (1996). With 
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Proceedings of EACL '99 
the help of a finite state recognizer mildly devi- 
ating strings are identified and correct versions 
are presented to the learner if necessary. The 
syntactic check follows a rather traditional path. 
The main work is done by a LFG-parser (Bresnan 
and Kaplan, 1982), originally implemented by Av- 
ery Andrews (Australian National University) and 
now modified to suit the needs of error detection 
with the help of modified grammar processing in- 
cluding error rules (Kriiger et al., 1997). As a 
next step the analysis of the sentence is checked 
against a world knowledge base, from which feed- 
back follows to the learner if the sentence does not 
match the internal model of the world. In con- 
trast to the dialogue knowledge base this model 
of the world cannot be altered by the learner be- 
cause of its usage for inference and the absence of 
a consistency-checking module to prevent contra- 
dictions etc. If the student has made an error, the 
system provides feedback to support the learner 
in typing a syntactically correct or semantically 
more plausible sentence. After this step the dia- 
logue component tries to find a reaction to con- 
tinue the dialogue, as described above. 
The main focus in all the analyses is to continue 
the dialogue but without ignoring the errors made 
by the learner. Only the orthographic check will 
actually interrupt the dialogue with a suggestion 
of correct words for the misspelled items. In all 
other cases the dialogue partner will react to the 
erroneous input depending on the type of error. 
4 Conclusion 
The aim of this concept is to provide a foreign lan- 
guage learner with exercises that enhance his/her 
communicative ability. One important module in 
the system is the diMogue component itself which 
organizes e.g. the turntaking in a dialogue. At 
least two knowledge sources are necessary for li- 
mited flexibility and reusability. The structure of 
a dialogue can be handled by a discourse gram- 
mar whereas the content of a diMogue is stored 
into an (expandable) knowledge base. To add new 
dialogues, only the dialogue knowledge base and 
the surrounding discourse grammar have to be 
updated whereas the other specialized discourse 
grammars can be reused. The structure of the 
dialogue knowledge base could also allow for the 
handling of questions and multi-sentence answers 
given by the learner. Nodes in the tree do not 
only represent variables to be instantiated by the 
learner, but might also include knowledge the sys- 
tem can provide to the learner. 
The kind of dialogue component presented here 
allows for 1) easy maintainance and expansion 
with new dialogues, 2) advanced feedback to the 
learner and 3) flexible and pedagogically sound 
exercises which enhance the process of aquiring 
'communicative competence'. 
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