Dialogue research tends to distinguish between chit-chat and goal-oriented tasks. While the former is arguably more naturalistic and has a wider use of language, the latter has clearer metrics and a more straightforward learning signal. Humans effortlessly combine the two, and engage in chit-chat for example with the goal of exchanging information or eliciting a specific response. Here, we bridge the divide between these two domains in the setting of a rich multi-player text-based fantasy environment where agents and humans engage in both actions and dialogue. Specifically, we train a goal-oriented model with reinforcement learning via self-play against an imitation-learned chit-chat model with two new approaches: the policy either learns to pick a topic or learns to pick an utterance given the top-k utterances. We show that both models outperform a strong inverse model baseline and can converse naturally with their dialogue partner in order to achieve goals.
Agents interact (speak, act) and can achieve goals in a rich world with diverse language, bridging the gap between chit-chat and goal-oriented dialogue. This paper studies a multiagent dialog task in which the learning agent aims to generate natural language actions that elicit a particular action from the other agent, and shows RL-agents can achieve higher task completion levels than imitation learning baselines. This paper explores the goal-oriented dialogue setting with reinforcement learning in a Fantasy Text Adventure Game and observes that the RL approaches outperform supervised learning models.