  We study non-collaborative dialogs, where two agents have a conflict of
interest but must strategically communicate to reach an agreement (e.g.,
negotiation). This setting poses new challenges for modeling dialog history
because the dialog's outcome relies not only on the semantic intent, but also
on tactics that convey the intent. We propose to model both semantic and tactic
history using finite state transducers (FSTs). Unlike RNN, FSTs can explicitly
represent dialog history through all the states traversed, facilitating
interpretability of dialog structure. We train FSTs on a set of strategies and
tactics used in negotiation dialogs. The trained FSTs show plausible tactic
structure and can be generalized to other non-collaborative domains (e.g.,
persuasion). We evaluate the FSTs by incorporating them in an automated
negotiating system that attempts to sell products and a persuasion system that
persuades people to donate to a charity. Experiments show that explicitly
modeling both semantic and tactic history is an effective way to improve both
dialog policy planning and generation performance.
