A Mixture-of-Expert Approach to RL-based Dialogue Management. (arXiv:2206.00059v1 [cs.CL])

Despite recent advancements in language models (LMs), their application to
dialogue management (DM) problems and ability to carry on rich conversations
remain a challenge. We use reinforcement learning (RL) to develop a dialogue
agent that avoids being short-sighted (outputting generic utterances) and
maximizes overall user satisfaction. Most existing RL approaches to DM train
the agent at the word-level, and thus, have to deal with a combinatorially
complex action space even for a medium-size vocabulary. As a result, they
struggle to …

arxiv expert management rl