  End-to-end task-oriented dialog models have achieved promising performance on
collaborative tasks where users willingly coordinate with the system to
complete a given task. While in non-collaborative settings, for example,
negotiation and persuasion, users and systems do not share a common goal. As a
result, compared to collaborate tasks, people use social content to build
rapport and trust in these non-collaborative settings in order to advance their
goals. To handle social content, we introduce a hierarchical intent annotation
scheme, which can be generalized to different non-collaborative dialog tasks.
Building upon TransferTransfo (Wolf et al. 2019), we propose an end-to-end
neural network model to generate diverse coherent responses. Our model utilizes
intent and semantic slots as the intermediate sentence representation to guide
the generation process. In addition, we design a filter to select appropriate
responses based on whether these intermediate representations fit the designed
task and conversation constraints. Our non-collaborative dialog model guides
users to complete the task while simultaneously keeps them engaged. We test our
approach on our newly proposed ANTISCAM dataset and an existing
PERSUASIONFORGOOD dataset. Both automatic and human evaluations suggest that
our model outperforms multiple baselines in these two non-collaborative tasks.

Computation and Language;Artificial Intelligence;Human-Computer Interaction