A hierarchical approach for efficient multi-intent dialogue policy learning

This paper proposes a hierarchical method for learning an efficient Dialogue Management (DM) strategy for task-oriented conversations serving multiple intents of a domain. Deep Reinforcement Learning (DRL) networks specializing in individual intents communicate with each other, having the capability of sharing overlapping information across intents. The sharing of information across state space and the presence of global slot tracker prohibits the agent to reask known information. Thus, the system is able to handle sub-dialogues based on subset of intents covered by different Reinforcement Learning (RL) models, thereby, completing the dialogue without again asking already provided information common across intents. The developed system has been demonstrated for Air Travel domain. The experimental results indicate that the developed system is efficient, scalable and can serve multiple intents based dialogues adequately. The proposed system when applied to 5-intent dialogue systems attains an improvement of 41% in terms of dialogue length as compared to a single-intent based system serving the same 5-intents.

Automated summary

The paper presents a hierarchical method for efficient Dialogue Management (DM) strategy using Deep Reinforcement Learning (DRL) networks in task-oriented conversations. The system is scalable and capable of handling multiple intents, demonstrating a 41% improvement in dialogue length for a 5-intent dialogue system compared to a single-intent system.