  Maintaining engagement and consistency is particularly important in dialogue
systems. Existing works have improved the performance of dialogue systems by
intentionally learning interlocutor personas with sophisticated network
structures. One issue with this approach is that it requires more personal
corpora with annotations. Additionally, these models typically perform the next
utterance prediction to generate a response but neglect the discourse coherence
in the entire conversation. To address these issues, this study proposes a
method of learning to memorize entailment and discourse relations for
persona-consistent dialogue tasks. Entailment text pairs in natural language
inference dataset were applied to learn latent entailment relations as external
memories by premise-to-hypothesis generation task. Furthermore, an internal
memory with a similar architecture was applied to the discourse information in
the dialogue. Placing orthogonality restrictions on these two memory spaces
ensures that the latent entailment relations remain dialogue-independent. Both
memories collaborate to obtain entailment and discourse representation for the
generation, allowing a deeper understanding of both consistency and coherence.
Experiments on two large public datasets, PersonaChat and DSTC7-AVSD,
demonstrated the effectiveness of the proposed method. Both automatic and human
evaluations indicate that the proposed model outperforms several strong
baselines in terms of both persona consistency and response coherence. Our
source code is available at https://github.com/Chenrj233/LMEDR.
