  Dialogue policy optimization often obtains feedback until task completion in
task-oriented dialogue systems. This is insufficient for training intermediate
dialogue turns since supervision signals (or rewards) are only provided at the
end of dialogues. To address this issue, reward learning has been introduced to
learn from state-action pairs of an optimal policy to provide turn-by-turn
rewards. This approach requires complete state-action annotations of
human-to-human dialogues (i.e., expert demonstrations), which is labor
intensive. To overcome this limitation, we propose a novel reward learning
approach for semi-supervised policy learning. The proposed approach learns a
dynamics model as the reward function which models dialogue progress (i.e.,
state-action sequences) based on expert demonstrations, either with or without
annotations. The dynamics model computes rewards by predicting whether the
dialogue progress is consistent with expert demonstrations. We further propose
to learn action embeddings for a better generalization of the reward function.
The proposed approach outperforms competitive policy learning baselines on
MultiWOZ, a benchmark multi-domain dataset.
