  Effective interlocutors account for the uncertain goals, beliefs, and
emotions of others. But even the best human conversationalist cannot perfectly
anticipate the trajectory of a dialogue. How well can language models represent
inherent uncertainty in conversations? We propose FortUne Dial, an expansion of
the long-standing "conversation forecasting" task: instead of just accuracy,
evaluation is conducted with uncertainty-aware metrics, effectively enabling
abstention on individual instances. We study two ways in which language models
potentially represent outcome uncertainty (internally, using scores and
directly, using tokens) and propose fine-tuning strategies to improve
calibration of both representations. Experiments on eight difficult negotiation
corpora demonstrate that our proposed fine-tuning strategies (a traditional
supervision strategy and an off-policy reinforcement learning strategy) can
calibrate smaller open-source models to compete with pre-trained models 10x
their size.
