  How to effectively utilize the dialogue history is a crucial problem in
multi-turn dialogue generation. Previous works usually employ various neural
network architectures (e.g., recurrent neural networks, attention mechanisms,
and hierarchical structures) to model the history. However, a recent empirical
study by Sankar et al. has shown that these architectures lack the ability of
understanding and modeling the dynamics of the dialogue history. For example,
the widely used architectures are insensitive to perturbations of the dialogue
history, such as words shuffling, utterances missing, and utterances
reordering. To tackle this problem, we propose a Ranking Enhanced Dialogue
generation framework in this paper. Despite the traditional representation
encoder and response generation modules, an additional ranking module is
introduced to model the ranking relation between the former utterance and
consecutive utterances. Specifically, the former utterance and consecutive
utterances are treated as query and corresponding documents, and both local and
global ranking losses are designed in the learning process. In this way, the
dynamics in the dialogue history can be explicitly captured. To evaluate our
proposed models, we conduct extensive experiments on three public datasets,
i.e., bAbI, PersonaChat, and JDC. Experimental results show that our models
produce better responses in terms of both quantitative measures and human
judgments, as compared with the state-of-the-art dialogue generation models.
Furthermore, we give some detailed experimental analysis to show where and how
the improvements come from.
