Research on Multi-turn Dialogue Generation Strategy Guided by Topic

P. Zhang, Hongrong Wang, Jie Wang
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Abstract

The open-domain generated dialogue model relies on masssively the neural network model to generate sentences without grammatical errors, and does not consider effective mechanisms to manage chat topics, resulting in monotonous and incoherent conversation topics. Inspired by human’s dialogue strategy, this paper proposes a topic-guided multi-turn dialogue generation strategy, DATHRED, which with a knowledge topic smoothing technology. It uses HRED to model multiple turns of dialogue, and proposes the two-way confrontation model to improve the topic richness of multi-turn dialogue and the fluency of topic transition. The comparison with the baseline model on the KdConv dataset verifies the effectiveness of our method.
话题导向的多回合对话生成策略研究
开放域生成对话模型大量依赖神经网络模型生成无语法错误的句子,没有考虑有效的聊天话题管理机制,导致对话话题单调、不连贯。受人类对话策略的启发,本文提出了一种主题导向的多回合对话生成策略,该策略采用知识主题平滑技术。利用HRED对多回合对话进行建模,提出双向对抗模型,提高多回合对话的话题丰富性和话题转换的流畅性。通过与KdConv数据集上的基线模型的比较,验证了该方法的有效性。
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