  Task-oriented dialogue systems are designed to achieve specific goals while
conversing with humans. In practice, they may have to handle simultaneously
several domains and tasks. The dialogue manager must therefore be able to take
into account domain changes and plan over different domains/tasks in order to
deal with multidomain dialogues. However, learning with reinforcement in such
context becomes difficult because the state-action dimension is larger while
the reward signal remains scarce. Our experimental results suggest that
structured policies based on graph neural networks combined with different
degrees of imitation learning can effectively handle multi-domain dialogues.
The reported experiments underline the benefit of structured policies over
standard policies.
