  Recent advances in sequence-to-sequence learning reveal a purely data-driven
approach to the response generation task. Despite its diverse applications,
existing neural models are prone to producing short and generic replies, making
it infeasible to tackle open-domain challenges. In this research, we analyze
this critical issue in light of the model's optimization goal and the specific
characteristics of the human-to-human dialog corpus. By decomposing the black
box into parts, a detailed analysis of the probability limit was conducted to
reveal the reason behind these universal replies. Based on these analyses, we
propose a max-margin ranking regularization term to avoid the models leaning to
these replies. Finally, empirical experiments on case studies and benchmarks
with several metrics validate this approach.
