We demonstrate a prototype allowing the unsupervised modeling of the structure of task-oriented dialogues. The prototype aims to assist the conception of a conversational agent architecture. The graphical representation displays the main stages of the dialogues and the transitions between them. Our tool allows to manipulate this graphical representation. We detail the various functionalities demonstrated.

1. Semi-Automated Assistance for Conceiving
Chatbots
Jean-Leon Bouraoui1
and Vincent Lemaire1
2 Avenue Pierre Marzin, 22300 Lannion, France
{jeanleon.bouraoui,vincent.lemaire}@orange.com
Abstract. We demonstrate a prototype allowing the unsupervised mod-
eling of the structure of task-oriented dialogues. The prototype aims to
assist the conception of a conversational agent architecture. The graph-
ical representation displays the main stages of the dialogues and the
transitions between them. Our tool allows to manipulate this graphical
representation. We detail the various functionalities demonstrated.
Keywords: Dialog Systems · Co-clustering · Graphs.
1 Introduction
In artificial intelligence field, dialog systems are gaining popularity with the
general public; especially as they benefit from advances in understanding of
the contents and contexts. Mobile and home applications such as Siri (Apple),
Google Assistant (Google), Cortana (Microsoft) or Alexa (Amazon) are the most
popular. To quantify this growing interest in the technology of dialog interfaces,
and dialog systems in particular, let us cite the recent study by the analyst firm
Gartner 1
. It places dialog systems among the 10 strategic technologies for 2018.
One of the current trends is to propose software devices to assist the design of
dialog systems. These devices are customizable according to the conceiver needs,
and the field of application (for example, reservation of trips, ordering of products
or services, etc.). One of the matters of these devices is that they can hardly be
set up quickly. Indeed, there is currently no generic system, and the adaptation
of a dialog system to a given application field is time consuming. Most of the
existing solutions do not provide a robust possibility to set up more quickly the
system. Some solutions allows to design the dialog architecture through a visual
interface. But still the conceiver has to manually perform this task based on his
knowledge of the domain.
In this context, we present a semi-automatic assistance solution for the cre-
ation or adaptation of a dialog system for any application field, without prior
knowledge. We describe the details of this prototype, and its main innovative
features.
1
https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-
trends-for-2018

2. 2 J.L. Bouraoui et al.
2 Description of the Problem
We denominate a dialog as an exchange of information between two interlocutors
(knowing that a dialog can involve more than two interlocutors). An interlocutor
can be a human or a machine (in a broad sense: an artificial system, software
or hardware). We are interested in the finalized dialogs, which aim to achieve a
goal: the interlocutors will collaborate to achieve this goal.
We call “dialog corpus” a set of n dialogs relating to a particular domain.
Such a set can be composed, for instance, of transcripts of train reservation
dialogs, or of interaction chats between a phone provider advisor and a client.
Each dialog is composed of t speech turns; a speech turn corresponds to what is
said by one of the interlocutors without any interruption (usually one or more
sentences).
Our goal is to automatically determine, within each dialogue: (i) the different
phases of the dialogue (including the expressed intentions, we will now designate
them by the term “ theme ”, which corresponds to themes generic as to sub-
goals of the dialogue); (ii) transitions between phases. The goal is to obtain an
oriented graph showing the main transitions between themes.
The graph thus obtained presents multiple interests. The principal is the
initialization of the dialogue agent’s design: it can serve as a basis for modeling
the architecture of a specialized dialogue agent on the target domain, and thus
facilitate its execution. At present, this task is mostly done manually: either a
priori, from the representation that the designer has dialogues possible on a task
and a given field; either a posteriori, from the consultation of existing corpora;
in both cases, the process is time consuming.
In addition, the graph, as well as the steps taken to obtain it, will allow
the designer, without prior knowledge of the application domain, to have a first
understanding of the thematic content of the dialogues, their structuring, and
more generally the knowledge. the most relevant information for the realization
of the dialogue agent.
3 Description of our solution
For the demonstration at the conference, we will use the following usecase: a
designer wants to set up a dialoging agent for a specific application domain. He
has a corpus of dialogs relating to this field. First, he will use the backend of
our tool to identify, unsupervised and without annotation, the underlying dialog
phases, and their transitions. He will then manipulate the graphs obtained using
the visual interface provided. We describe these different steps below.
3.1 Engineering the backend
Beforehand, the corpus is filtered in order to remove the “stopwords”. They are
words that do not convey semantic information (like prepositions, articles ...).
We used the list of “stopwords” provided by the NLTK library 2
.
2
http://www.nltk.org/nltk data

3. Semi-Automated Assistance for Conceiving Chatbots 3
To identify the dialogue phases, a CoClustering technique is used to obtain a
“copartition” of the word matrix x turn of speech. Given two or more categorical
or numerical variables, we perform a simultaneous partitioning of the variables:
the values of categorical variables are grouped into clusters and the numerical
variables are partitioned into intervals - which amounts to a coclustering prob-
lem. The method used is based on the MODL approach described in [1]. It then
remains to determine the transitions between the dialogue phases.
In our approach, a phase corresponds to a cluster of turns of speech, homo-
geneous in relation to a given theme. A phase defined this way can be connected
to one or more others, according to the observed frequency of their successions
in the dialogues of the corpus.
CoClustering initially loses the sequentiality of the dialogues, since the speech
turns are grouped by themes, regardless of their order in the dialogue. To find
this temporal aspect, the backend then projects the cluster identifiers on each
corresponding turn of speech. The resulting representation is an oriented graph,
whose nodes are the clusters, and the arcs are the successions between clusters.
3.2 Presentation of the frontend
The user interface makes it possible to visualize interactively and in real time
the data processed in the backend, in the form of graphs. We describe the main
features below.
Real Time and Interactive Display
– Choice of the granularity of display of the graph, according to the number
of clusters and / or their relations, and dynamic modification of the corre-
sponding graph;
– Possibility to manipulate the graphs with the mouse pointer: for example to
“pull” a cluster away from others, to select one or more clusters, to zoom in
and out, and so on.
– Showing cluster Names , their number, and the frequency of each relationship
(possibly with percent display on all dialogues)
– Ability to rename clusters, and add names to relationships between clusters;
– Ability to browse through the contents of a cluster, consisting of several
rounds of speech. These can be displayed as a list, possibly with additional
information.
Manipulation of the dialogue architecture: These features make it possible
to modify the local or global architecture of the graph. The designer of the
interacting agent can thus adapt and refine the architecture according to his
needs. It is possible to modify:
– The contents of a given cluster. Notably by deleting one or several speech
turns that would not be thematically homogeneous with the cluster.

4. 4 J.L. Bouraoui et al.
– The architecture itself. Two main features are available. One is the fusion
of two clusters (for example if they are thematically similar and therefore
redundant). The other is the ability to select multiple speech towers of a
given cluster and to transfer them to a new cluster; it amounts to split in
two the current cluster. This is interesting in the case where the speech turns
are semantically similar, but heterogeneous with respect to the main theme
expressed in the cluster. If any of these features are used, the display of the
number of clusters and their relationships is updated dynamically.
Fig. 1. A chatbot architecture displayed by our solution
We used this prototype for several real usescase, including the one described
in [2]. Figure 1 shows the current version of the prototype frontend. It will be
presented in more detail during the demonstration. A video is available at the
url: https://www.dailymotion.com/video/x6j6xi9 3
.
References
1. Boull´e, M.: Data grid models for preparation and modeling in supervised learning.
Hands-On Pattern Recognition: Challenges in Machine Learning, volume 1,Guyon,
I. and Cawley, G. and Dror, G. and Saffari, A., 99-130, Microtome Publishing, (2011)
2. Bouraoui, J.L., Lemaire, V. : Cluster-Based Graphs for Conceiving Dialog Systems.
Workshop DMNLP at European Conference on Machine Learning (ECML), Skopje
(2017)
3
NB: the display of the prototype is currently in French; it will be translated in
English in the definitive version.