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Communication Dans Un Congrès Année : 2019

Goal-oriented dialogue systems : state-of-the-art and future works

Les systèmes de dialogue orientés-but : état de l'art et perspectives d'amélioration

Résumé

The management and selection of relevant information for a given speaking tour remains a problem for open-domain dialogue systems. For the latter, the possible interactions between a user and an agent are a priori infinite and indefinite. The possibility of an incorrect response from the agent to the user therefore remains high. For goal-oriented systems, the problem is considered solved, but in our experience no system shows remarkable robustness when evaluated in real situations. In this article, we present a state of the art of agent learning methods and different conversational agent models. In our opinion, one of the agent's avenues for improvement lies in his memory, because the latter (often represented by the triplet: current speaking time, dialogue history and knowledge base) is not yet modelled with enough precision. By providing the agent with a cognitively inspired memory model, we believe we can increase the performance of a goal-oriented dialogue system in real life situations, by using automatic learning algorithms with an antagonistic approach in support of a new memory model for the agent. ABSTRACT Goal-oriented dialogue systems: a recent overview and research prospects Determining which information is relevant for a given dialogue turn is still a problem for open-domain dialogue systems since the interaction between a user and the system is a priori infinite and indefinite. Thus, the possibility for error remains high for the dialog system. Although the problem is considered solved, to our experience, no system has shown outstanding performance when confronted to "real-world" situation. We review the state-of-the-art of machine learning approaches used for dialogue system development and present the various models of agent architecture. We claim that one of the main directions for improvement is the agent architecture. Often the agent's memory (represented by the triple: dialogue turn, dialogue history and knowledge base) is not modeled accurately enough. Thus we decided to investigate more cognitive oriented models of memory. We hypothesize that combining machine learning approaches in an adversarial setup with a new memory model for the agent would result in improved performances for goal-oriented dialogue systems. KEYWORDS: dialogue systems, learning by reinforcement, memory model, learning by antagonism, goal-oriented systems.
La gestion et la sélection des informations pertinentes pour un tour de parole donné restent un problème pour les systèmes de dialogue à domaine ouvert. Pour ces derniers, les interactions possibles entre un utilisateur et un agent sont a priori infinies et indéfinies. La possibilité d'une réponse erronée de l'agent à l'utilisateur demeure donc élevée. Pour les systèmes orientés-but, le problème est considéré comme résolu, mais d'après notre expérience aucun système ne montre une robustesse remarquable lorsqu'il est évalué en situation réelle. Dans cet article, nous dressons un état de l'art des méthodes d'apprentissage de l'agent et des différents modèles d'agent conversationnel. Selon nous, l'une des pistes d'amélioration de l'agent réside dans sa mémoire, car cette dernière (souvent représentée par le triplet : tour de parole courant, historique du dialogue et base de connaissances) n'est pas encore modélisée avec assez de précision. En dotant l'agent d'un modèle de mémoire d'inspiration cognitive, nous pensons pouvoir augmenter les performances d'un système de dialogue orienté-but en situation réelle, par l'emploi d'algorithmes d'apprentissage automatique avec une approche antagoniste en support d'un nouveau modèle de mémoire pour l'agent. ABSTRACT Goal-oriented dialog systems : a recent overview and research prospects Determining which information is relevant for a given dialog turn is still a problem for open-domain dialog systems since the interaction between a user and the system is a priori infinite and indefinite. Thus, the possibility for error remains high for the dialog system. Although the problem is considered solved, to our experience, no system has shown outstanding performance when confronted to "real-world" situation. We review the state-of-the-art of machine learning approaches used for dialog system development and present the various models of agent architecture. We claim that one of the main directions for improvement is the agent architecture. Often the agent's memory (represented by the triple : dialog turn, dialogue history and knowledge base) is not modeled accurately enough. Thus we decided to investigate more cognitive oriented models of memory. We hypothesize that combining machine learning approaches in an adversarial setup with a new memory model for the agent would result in improved performances for goal-oriented dialog systems. MOTS-CLÉS : systèmes de dialogue, apprentissage par renforcement, modèle de mémoire, appren-tissage par antagonisme, systèmes orientés-buts.
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Dates et versions

hal-02180287 , version 1 (12-07-2019)

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  • HAL Id : hal-02180287 , version 1

Citer

Léon-Paul Schaub, Cyndel Vaudapiviz. Les systèmes de dialogue orientés-but : état de l'art et perspectives d'amélioration. RECITAL, Jul 2019, Toulouse, France. ⟨hal-02180287⟩
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