Faculté des Sciences appliquées
Faculté des Sciences appliquées

Intention detection in chatbots

Klapka, Ivan ULiège
Promotor(s) : Ittoo, Ashwin ULiège
Date of defense : 6-Sep-2021/7-Sep-2021 • Permalink :
Title : Intention detection in chatbots
Translated title : [fr] Detection d'intention dans les chatbots
Author : Klapka, Ivan ULiège
Date of defense  : 6-Sep-2021/7-Sep-2021
Advisor(s) : Ittoo, Ashwin ULiège
Committee's member(s) : Wehenkel, Louis ULiège
Drion, Guillaume ULiège
Language : English
Number of pages : 61
Keywords : [en] Intent classification
[en] Transformers
[en] Goal-oriented chatbot
[en] Deep learning
Discipline(s) : Engineering, computing & technology > Computer science
Target public : Researchers
General public
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées


[en] As chatbots evolved over the years, researchers realised they could be used in a number
of different applications, ranging from customer service to social companions. This phenomenon led to companies investing resources into the development of their own chatbot.
Although successful implementations can revolutionize their domain of application, their
creation requires expertise and are often very time consuming to produce.
This work explores and explains different methods and mechanisms used for making
chatbots while focusing its attention on the intention detection task. A review of the state
of the art models is conducted with the objective of determining the best performing intent
classifier and look for ways to improve them. Another objective of this work is to find an
effective goal-oriented architecture for chatbots and provide a working example.
A series of experiments aimed at evaluating the performance of neural network models on intent classification has been conducted over multiple different dataset of varying
characteristic. An attempt at improving classification by first identifying domains has
also been attempted. In addition, the overall chatbot accuracy has been compared for
different models taking the role of intent classifier on the hybrid architecture.
The results have highlighted the fact that the performances are closely related to the
quality and quantity of the training data. Despite this fact, the BERT model seems to
perform better overall but it is not systematically better, especially over dataset with a
high number of possible intent and limited utterances. When it comes to the chatbot
architecture, everything depends on the complexity and requirement expected from the
chatbot. Nevertheless, the explored hybrid architecture appears to be a good compromise
between data requirement, scalability and performance.
In the end, this paper concludes by discussing the assumptions made and the limitations
of the systems studied as well as proposing different ideas for future works.



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  • Klapka, Ivan ULiège Université de Liège > Master ingé. civ. info., à fin.


Committee's member(s)

  • Wehenkel, Louis ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Méthodes stochastiques
    ORBi View his publications on ORBi
  • Drion, Guillaume ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
    ORBi View his publications on ORBi
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  • Total number of downloads 6

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