Despite the recent advancements in dialogue systems, persona-driven chatbots are still in their infancy. Previous studies on persona-driven dialogue generation demonstrated its ability in generating responses that contain more detailed persona information. However, the challenge of maintaining persona consistency and contextual coherence still persists in persona-driven dialogue generation. Moreover, current methods have limitations in processing multi-source inputs and identifying interlocutor intents due to the absence of trustworthy labels and effective modeling. Additionally, numerous approaches rely on pre-trained large-scale language models that require costly computational resources. To address these challenges, a lightweight hierarchical intent-inferring pointer network is proposed for multi-source persona-driven dialogue generation. The proposed method involves detecting interlocutor intents in chitchat and utilizing pseudo labeling and natural language inference techniques to generate intent labels. Our model is evaluated on a benchmark dataset PersonaChat. The experimental results show that our model outperforms the strongest baseline by 13.47% and 4.28% in terms of persona consistency and contextual coherence, respectively.

中文翻译：

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HIPPL：具有伪标记的分层意图推理指针网络，用于一致的角色驱动对话生成 [Research Frontier]


尽管对话系统最近取得了进步，但角色驱动的聊天机器人仍处于起步阶段。之前对角色驱动对话生成的研究表明，它能够生成包含更详细角色信息的响应。然而，在角色驱动的对话生成中，保持角色一致性和上下文连贯性的挑战仍然存在。此外，由于缺乏可信的标签和有效的建模，当前的方法在处理多源输入和识别对话者意图方面存在局限性。此外，许多方法都依赖于预先训练的大规模语言模型，这些模型需要昂贵的计算资源。为了应对这些挑战，提出了一种轻量级的分层意图推理指针网络，用于多源角色驱动的对话生成。所提出的方法涉及检测 chitchat 中的对话者意图，并利用伪标记和自然语言推理技术生成意图标签。我们的模型在基准数据集 PersonaChat 上进行评估。实验结果表明，我们的模型在角色一致性和上下文连贯性方面分别比最强基线高出 13.47% 和 4.28%。