  Language models trained on large-scale corpora can generate remarkably fluent
results in open-domain dialogue. However, for the persona-based dialogue
generation task, consistency and coherence are also key factors, which are
great challenges for language models. Existing works mainly focus on valuable
data filtering, model structure modifying, or objective function designing,
while their improvements are limited and hard to generalize to all types of
pre-trained language models. However, we find that language models can produce
consistent and coherent responses if we consider enough generations. Thus, the
problems lay in large-scale response generation and target response selection.
In this work, a simple but effective two-stage SimOAP strategy is proposed,
i.e., over-sampling and post-evaluation. The over-sampling stage takes
large-scale responses from existing trained models efficiently via
off-the-shelf distilling and compressing methods, and the post-evaluation stage
selects a good response based on multiple well-designed evaluation metrics from
large-scale candidates. Experimental results show that the proposed plug-in
SimOAP strategy improves the backbone models and outperforms the baseline
strategies in both automatic and human evaluations.
