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About this paper
Thibault Cordier, Tanguy Urvoy, Fabrice Lefèvre, Lina M. Rojas-Barahona
Combining Graph Neural Networks (GNNs) with imitation learning provides a structured approach to manage multi-domain task-oriented dialogues. This method enhances policy learning by capturing relationships between domains and slots, achieving performance comparable to handcrafted policies and improving data efficiency.
Key insights
- Explicitly modeling the structure of dialogue states and their relationships with Graph Neural Networks (GNNs) is effective for handling multi-domain settings.
- Combining reinforcement learning (RL) with imitation learning (IL) offers benefits over either approach alone, effectively balancing exploration with expert guidance and addressing sparse rewards.
- Parameter sharing across domains within a unified GNN architecture enhances generalization and enables more effective transfer learning.
Summary by alphaXiv.
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