Title: Latent Intention Dialogue Models,
Abstract: Developing a dialogue agent that is capable of making autonomous decisions
and communicating by natural language is one of the long-term goals of machine
learning research. Traditional approaches either rely on hand-crafting a small
state-action set for applying reinforcement learning that is not scalable or
constructing deterministic models for learning dialogue sentences that fail to
capture natural conversational variability. In this paper, we propose a Latent
Intention Dialogue Model (LIDM) that employs a discrete latent variable to
learn underlying dialogue intentions in the framework of neural variational
inference. In a goal-oriented dialogue scenario, these latent intentions can be
interpreted as actions guiding the generation of machine responses, which can
be further refined autonomously by reinforcement learning. The experimental
evaluation of LIDM shows that the model out-performs published benchmarks for
both corpus-based and human evaluation, demonstrating the effectiveness of
discrete latent variable models for learning goal-oriented dialogues.