Lilia, A Showcase for Fast Bootstrap of Conversation-Like Dialogues Based on a Goal-Oriented System

  • Matthieu RiouEmail author
  • Bassam Jabaian
  • Stéphane Huet
  • Fabrice Lefèvre
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11816)


Recently many works have proposed to cast human-machine interaction in a sentence generation scheme. Neural networks models can learn how to generate a probable sentence based on the user’s statement along with a partial view of the dialogue history. While appealing to some extent, these approaches require huge training sets of general-purpose data and lack a principled way to intertwine language generation with information retrieval from back-end resources to fuel the dialogue with actualised and precise knowledge. As a practical alternative, in this paper, we present Lilia, a showcase for fast bootstrap of conversation-like dialogues based on a goal-oriented system. First, a comparison of goal-oriented and conversational system features is led, then a conversion process is described for the fast bootstrap of a new system, finalised with an on-line training of the system’s main components. Lilia is dedicated to a chit-chat task, where speakers exchange viewpoints on a displayed image while trying collaboratively to derive its author’s intention. Evaluations with user trials showed its efficiency in a realistic setup.


Spoken dialogue systems Chatbot Goal-oriented dialogue system On-line learning 



This workshop has been partially supported by grants ANR-16-CONV-0002 (ILCB), ANR-11-LABX-0036 (BLRI).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Matthieu Riou
    • 1
    Email author
  • Bassam Jabaian
    • 1
  • Stéphane Huet
    • 1
  • Fabrice Lefèvre
    • 1
  1. 1.LIA/CERIAvignon UniversityAvignonFrance

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