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.

翻译：在对话系统中，维持参与度与一致性尤为重要。现有研究通过利用复杂网络结构有意识学习对话者人格特征，提升了对话系统性能，但该方法存在两大局限：一是需要大量带标注的人格语料，二是此类模型通常通过预测下一句生成回复，却忽视了整段对话的语篇连贯性。针对这些问题，本研究提出一种面向一致人格对话任务的蕴含与语篇关系记忆学习方法。首先，利用自然语言推理数据集中的蕴含文本对，通过前提到假设生成任务学习潜在蕴含关系作为外部记忆；其次，采用类似架构的内部记忆处理对话中的语篇信息；最后，对这两个记忆空间施加正交约束以确保潜在蕴含关系保持对话独立性。两种记忆协同获取蕴含与语篇表征以驱动生成过程，从而实现对一致性与连贯性的深层理解。在PersonaChat和DSTC7-AVSD两个大型公开数据集上的实验表明，所提方法具有有效性。自动评估与人工评估结果均显示，本文模型在人格一致性与回复连贯性方面显著优于多个强基线模型。源代码已开源至https://github.com/Chenrj233/LMEDR。