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

Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and Strategic Dialog History