A. Loisel, N. Chaignaud and J-Ph. Kotowicz
LITIS Laboratory - EA 4108 - Place Emile Blondel - BP 08 - 76131 Mont-Saint-Aignan Cedex, France
Keywords: Human-computer dialog, human factors, intelligent user interface, intelligent agent, corpus analysis.
Abstract: This article presents the Cogni-CISMeF project, which aims at improving the health information search
engine CISMeF, by including a conversational agent that interacts with the user in natural language. To
study the cognitive processes involved during information search, a bottom-up methodology was adopted.
An experiment has been set up to obtain human dialogs related to such searches. The analysis of these
dialogs underlines the establishment of a common ground and accommodation effects to the user. A model
of artificial agent is proposed, that guides the user by proposing examples, assistance and choices.
CISMeF (French acronym for “Catalog and Index of
French-language health resources”
aims at describing and indexing the main French-
language health resources in order to assist health
professionals and consumers in their search for
electronic information available on the Internet. To
index resources, CISMeF uses four different
concepts: meta-term, keyword, subheading and
resource type. It contains a thematic index, including
medical specialties, and an alphabetic index.
Nowadays, the system includes a graphic user-
interface, a query language and uses index and
thesaurus to find information. However, the
“extended” and the “boolean” search options
increase the complexity of the interface and users
are not comfortable with it.
The aim of the Cogni-CISMeF project is to
improve search in CISMeF by including a
conversational agent that interacts with the user in
natural language. This agent leads the user in his
information search by analyzing his aims and by
proposing, assistance and choices. Once recognized,
the user’s intention is translated into queries.
In order to adapt the system to the user, we
believe that the human-computer interactions shall
be designed to mimic human interactions. To this
end, an experiment has been set up to obtain human
dialogs between a CISMeF expert and users looking
for health information. These dialogs (constituting a
corpus) have been analyzed to extract their
discursive structure and their linguistic features in
order to build a cognitive model of a conversational
In this article, Section 2 describes related work
on dialog systems. Section 3 details the
psychological experiment we have set up and the
corpus collection. The analysis of the corpus is
presented in Section 4 and Section 5 describes the
cognitive model that we propose, according to these
results. In Section 6, conclusion and perspectives
close this paper.
Theories used by human-computer dialog systems
can be classified into several categories. One
possibility is to assess whether they are based on the
agent intention or on social conventions.
2.1 Intention based Approaches
Intention based approaches use a representation of
the mental states of the artificial agent. The most
famous model is BDI (Belief, Desire and Intention).
which has been used both in logic (Cohen and
Levesque, 1990) and planning (Allen and Perrault,
1980) settings. Its implementation is complex and its
reuse is domain restricted.
Loisel A., Chaignaud N. and Kotowicz J. (2008).
In Proceedings of the Tenth International Conference on Enterprise Information Systems - HCI, pages 227-232
DOI: 10.5220/0001698402270232
2.2 Convention based Approaches
To simplify, a dialog can be considered as a protocol
represented by finite state automata in which
transitions are the possible speech acts of the dialog.
The agent has no internal representation. These
approaches are rather rigid even if some of them
(Sitter and Stein, 1992) use recursive automata.
Another conventional model (Lewis, 1979)
consists in representing information shared during
the dialog (called “common ground”) in a
conversational board. This theory is more
descriptive than predictive and thus is difficult to
integrate into a dialog system.
2.3 Mixed Approaches
Dialog games (Levin and Moore, 1980) are
interested in social conventions between utterances.
They use structures, games for which interactions
are precisely described. Games are stereotypes that
model a communicational situation.
The QUD (Questions Under Discussion) model,
proposed by (Ginzburg, 1996) and totally
implemented in the GoDiS system (Larsson, 2002),
takes into account mainly the transmission of
missing information. The dialog uses both a
conversational board and internal representation of
the agent. This approach is mainly based on the
questions and their responses. Each speech act
(enunciated by the user or the system) modifies the
“information state” (IS), comprising a private part
and a public part.
With the “grounding” theory, (Traum, 1994)
proposes 5 modalities according to which an
utterance is grounded: perception, contact, semantic
understanding, pragmatic understanding, integration.
For each modality, there are speech acts of positive
(resp. negative) grounding if this modality is (resp.
is not) grounded. For example, if the perception is
grounded but not the semantic understanding, the
system can produce a repeating of the utterance to
show that it has been heard and then it can say a
speech act like “not understood”.
This approach is highly capable when it is added
with accommodation effects (Lewis, 1979) like in
GoDiS. When user utterances do not match with the
current plans, the system loads a new relevant plan
to this utterance. Plans can be performed in parallel.
At first, we wanted to model the reasoning of the
CISMeF chief librarian, when he was searching in
the CISMeF system. He was asked five questions
from health professionals and his answers have been
recorded. These records showed that the CISMeF
chief librarian has a complete understanding of the
user’s intention and suggests optimal queries.
However, he does not need to converse with the user
to understand his inquiry. We had thus to set up a
new experimentation dealing with the recording of
dialogue between a CISMeF expert and a user.
The users were voluntary members of the LITIS
laboratory (secretary, PhD students, researchers and
teachers) who wanted to obtain responses about
medical inquiries. The experts were two members of
our project, trained to the CISMeF system and
terminology. The experimentation took place as
follows: one expert and one user were facing a
computer using the advanced search interface of the
system and recording all the queries with their
answers in a log. The expert was in charge of
conducting the search by conversing with the user
and verbalizing each action, inquiry and answer. The
experimentation ended when relevant documents
were given to the user or when it seemed that no
answer existed in the system. A textual corpus was
constituted from the transcription of the twenty-one
dialogues recorded.
Moreover, following this experimentation, we
asked the CISMeF chief librarian to answer the
users’ inquiries and to verbalize his search process.
The verbal occurrences were also recorded. Our aim
was to obtain optimal queries to these questions
using the CISMeF terminology. They provide
explanations about the strategies adopted by the
chief librarian.
We have hand-analyzed the textual corpus. During
the conversations, experts tried to keep control of the
dialog by making the user repeat and confirm his
utterances to avoid ambiguity or contestation. Many
discursive tags (agreement, question, suggestion,
refusal…) lead to interaction. Several iterative loops
ensure the continuity of the dialog.
This analysis brings out a global structure of
dialogs broken down into sub-dialogs and it allows
to build a list of speech acts observed in the corpus.
4.1 Global Structure of Dialogs
In the dialogs, there are a lot of comings and goings
between the initial query of the user and the answers
of the system depending on the results. Moreover,
dialogs can be divided into sub-dialogs. Figure 1
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describes the possible links between sub-dialogs. A
dialog always begins with an opening sub-dialog,
which can indifferently be short or long. It consists
in identifying the user, presenting the CISMeF
system and negotiating the task. Then, the user can
ask the expert his medical inquiry in a querying sub-
dialog. The expert reformulates the question to be
sure of the tackled themes and the meaning of the
words used. The inquiry can be broken down into
several other inquiries that can be a question about a
definition or about explanation on the system itself.
In the case of an information inquiry, the expert
builds the query with the help of the user. Each term
constituting the query is discussed according to the
CISMeF terminology. Queries are performed and the
list of documents is presented to the user. One
particular document can be described. At any time,
these sub-dialogs can be interrupted by precision
inquiries. The dialog finishes with an ending sub-
dialog on the initiative of the user either with a
success (the documents are relevant) or with a
Figure 1: Links between sub-dialogs.
4.2 Taxonomy of Speech Acts
A list of speech acts has been built according to
linguistic features found into the corpus.
This taxonomy comes from (Weisser, 2003) and
has been adapted to our corpus. It follows the
illocutionary force of the speech acts.
Initiative assertives
Inform: to bring information without
expecting any response
(e.g. expert: “I think that the keyword
“parasomny” also exists”)
Initiative directives
RequestInfo: information query
(e.g. expert: “Do you think that we can find a
medical specialty?”)
Offer: to propose something that the
interlocutor can accept or refuse
(e.g. expert: “Do you want to try with the
keyword “general medicine”?”)
RequestDirective: the speaker expects
guidelines from the interlocutor
(e.g. expert: “What is your question?”)
Reactive assertives
Answer: response to a question
(e.g. expert: “There are to many
Accept: to agree with a previous utterance
that is both achieved and satisfied
(e.g. user: “Yes, exactly!”)
Refuse: to refuse a previous utterance that
is achieved but not satisfied
(e.g. user: “No, I am not interested”)
Acknowledge: to tell the interlocutor that
his utterance is achieved
(e.g. expert: “Ok! I understood the
WantsNothing: to answer negatively to a
(e.g. user: “No, I do not want anything else”)
Reactive directives
Confirm: request of utterance confirmation
(e.g. expert: “You want to know the process
to follow to donate an organ, don’t you?”)
Bye: to conclude the conversation and to
close the communication channel
(e.g. expert: “Bye, have a nice day!”)
Greet: to initiate a conversation or to
pursue it after a break
(e.g. expert: “Hello, what is your question?”)
InformIntent: to specify to the
interlocutor what we are about to do
(e.g. expert: “Well, let’s see if we can find
something about it”)
Some of these acts are explicit « grounding »
Accept, Acknowledge, WantsNothing,
Confirm, Refuse.
The analysis of these dialogs highlighted:
the breaking down of the dialogs into sub-
dialogs represented by plans;
the establishment of a common ground,
thanks to rewordings, agreements, questions;
a list of speech acts, classified according to
their illocutionary force and their content;
a classification of some of these acts as
positive or negative « grounding » acts;
accommodation effects on the user.
From the corpus analysis, our aim is to design a
software agent able to converse with a user and help
him to find information.
5.1 Agent Architecture
Our agent (Figure 2) is composed of 3 main
The language model, which receives the
user’s inquiry in natural language. It
performs a lexical and syntactical analysis
(using TreeTagger (Schmid, 1994) from
Stuttgart University), a pragmatic analysis
(from our speech act analyzer, which uses
linguistic tags — like tense, modality and
context — to assign speech acts to
utterances, thanks to a set of rules) and a
semantic analysis (identification of terms
from the CISMeF terminology).
The dialog model, which comprises the
dialog manager and the sentence generator
based on incomplete sentences.
The task model, which encapsulates the
CISMeF interface to access the medical
document base. It includes also a query
builder from the recognized terms and a
result interpreter.
Figure 2: Conversational agent architecture.
This agent is under development in Java. Our
dialog uses the implementation of GoDiS (Larsson,
2002) written in Prolog. We only describe here the
dialog manager.
5.2 Dialog Manager
The GoDiS system (Larsson, 2002) is well adapted
to our needs, since it is based on an explicit task and
requires no reasoning on users intention. However, it
uses a list of speech acts, which is less extensive
than ours: it misses acts like
Inform, Offer and
Suggest. These acts allow the system to propose
relevant information in an opportunistic way
according to the search.
5.2.1 Overview
Our dialog manager performs a set of plans to
produce speech acts. There exist two types of plans:
question plans (planQ), in the sense of QUD,
which aim at answering inquiries by
returning data;
action plans (
planA), which run a sequence
of actions.
The formalism uses the predicate logic with the
operator “?” to represent questions. There are three
types of questions:
the total inquiries: ?P,
the partial inquiries: ?P(x),
the inquiries with a list of choices:
?set(P1(x), P2(y), P3(z)).
Moreover, our dialog manager controls an
information state (IS) composed of a private part and
a public part.
The private part contains:
Agenda, actions of the current plan,
Bel, the knowledge of the system,
Plan, the current plan,
Nextmove, the next speech act to be
The public part is the conversational board:
Com, shared knowledge,
Issue, planQ in progress or idle,
Qud, focus on Issue,
Action, planA in progress or idle.
Plans use a list of actions that can produce
speech acts. This list comes partly from GoDiS:
findout(Q) to question with the speech act
Ask. The system repeats the question Q until
it is answered or aborted.
raise(Q) to question (only one time)
bind(Q) to answer the question Q without
posing the query.
assume(B) to add a predicate B to the
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assumeAction(A) to add a predicate A to
assumeIssue(I) to add a predicate I to
consultDB(Q) to interrogate the data base
and to add relevant information to
Bel to
make suggestions.
cooperativeSearch(p,l,r) to suggest
to the user information having a property
among a list
l in com. r is the result of the
search (
failure or success).
report(I) to say the speech act inform.
say(l) to say a speech act l,
loadPlan(p) to load a plan p to be
The predicate
PostCond(P,A) allows to
give the value
A to the predicate P.
Suggestions can interrupt these plans in an
opportunistic way. A rule base generates them
according to the IS. There exist three types of rules:
rules to update private or shared beliefs in
the IS,
rules to choose a speech act according to the
utterance just pronounced by the user,
strategies or meta-rules to choose the update
rules to be used during interactions: to
update the IS with the contents of the speech
act, to load plans from the plan library to
Plan, to use accomodation rules when a non
expected speech act is found, to move the
current action from
Plan to Agenda, to
clean the IS, to perform the action in
Each sub-dialog (Figure 1) is represented by a
dialog plan (
PlanQ or PlanA). We describe below
three of them: the opening plan, the queryAnalysis
plan and the
DocumentSearch plan.
5.2.2 Opening Plan
The Opening plan allows the system to initiate the
dialog with a prompt. Then the
plan is loaded.
5.2.3 QueryAnalysis Plan
The QueryAnalysis plan aims at gathering the
query of the user. If the user does not ask quickly his
question, the action
Findout allows the system to
ask for his goal (definitions, documents or
explanations about the system).
ifThen(not q)
When the user opens a dialog with the system
and submits directly his query (e.g. “Hello, I would
like to know if …”) in one sentence, an
accommodation rule allows the system to load two
plans successively (
Opening and QueryAnalysis
plans) to adapt itself to this single sentence.
5.2.4 DocumentSearch Plan
The DocumentSearch plan performs several steps
of the sub-dialog: it builds the query and submits it
to the database. Then, it evaluates the resulting
documents if any. It comprises several plans
described below.
This plan is special since it remains active in the
IS. The search can be refined to increase the number
of results or expanded to decrease the number of
results. This plan ends only with an agreement of the
user (with or without success).
ifThen( d Bel)
Post-condition: this plan remains active.
QueryBuilding plan
QueryBuilding plan includes four different
1. At the beginning of the search, from the initial
query, the system suggests keywords of the
CISMeF taxonomy thanks to the action
2. If the keywords found in the previous step are
not sufficient to find documents, the system
tries to refine the query by suggesting meta-
terms and subheadings. If it does not find any
term, it can ask to the user.
3. If not enough documents are found, the
system expands the query.
4. If too many documents are found, the system
refines the query.
The action
CooperativeAction determines
how to specify the inquiry to obtain relevant
documents: add or delete terms, use synonyms,
hyponyms, hyperonyms, etc.
(ifThen(not keyword(k) Com)
ifThen( keyword(k) Com
and NotEnoughDocument Com)
ifThen(not metaTerm(m) Com)
ifThen(not subheading(q) Com)
ifThen(NotEnoughDocument Com)
ifThen(NotEnoughDocument Com)
ifThenElse( term(t) Com)
ListEvaluation plan
ListEvaluation plan takes as input a set of
d and informs (as output) the user
whether the documents are numerous enough or not
according to the limit δ (min and max). If they are
sufficient, the plan loads the plan
DocumentDescription plan
DocumentDescription plan takes as input a
set of documents d, analyses their headers to decide
whether they are relevant to the user’s question. If
necessary, the user is also given a chance to assess
the relevance of the documents.
Suggestions can interrupt these plans in an
opportunistic way and trigger for example a plan
that explains the system. These suggestions are
generated by a set of rules according to the IS.
While(not interesting(x))
We adopted an interdisciplinary approach to design
a human-computer dialog system for health
information search. We collected and analyzed a
rich textual corpus on which the building of a
common ground and accommodation effects on the
user have been observed. Dialogs can be divided
into sub-dialogs, directly linked to the task. This
analysis allowed us to propose a cognitive model
based on the theories of “grounding” and
This model is under development. Once
implemented, our system will be tested with users on
the web to obtain human-computer dialogs, in order
to identify and fix its shortcomings.
The validation of our system consists in
evaluating the added value brought to CISMeF. The
idea is to compare the queries made by the user,
those proposed by the chief librarian and those built
using our dialog system. This comparison will be
made by calculating queries precision and recall.
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