Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation

Abstract

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 ..