论文标题
部分可观测时空混沌系统的无模型预测
Improving Personality Consistency in Conversation by Persona Extending
论文作者
论文摘要
以一致的性格赋予聊天机器人对于代理商提供类似人类互动的作用至关重要。但是，现有的个性化方法通常会鉴于用文本描述描绘的静态预定义角色会产生响应，这可能严重限制了人类和聊天机器人的互动性，尤其是当代理需要回答预定义的角色中的查询时，这是所谓的超出预先定义的人物问题（命名为OOP）（命名为OOP）。为了减轻问题，在本文中，我们提出了一种新型的检索到预测范式，包括两个亚构组成分，即（1）人格检索模型（PRM），它基于自然语言推断（NLI）模型从全球收藏中检索角色，底层人士与预先定义的人相处是一致的； （2）后验变压器（PS-Transformer）采用角色后部分布，进一步考虑了地面响应中使用的实际角色，从而最大程度地减轻了训练和推断之间的差距。此外，我们提出了一个称为IT-Convai2的数据集，该数据集首先突出了个性化对话中的OOP问题。对IT-CONVAI2和CORVAI2的广泛实验表明，我们提出的模型在自动指标和人类评估方面均可取得显着改善。
Endowing chatbots with a consistent personality plays a vital role for agents to deliver human-like interactions. However, existing personalized approaches commonly generate responses in light of static predefined personas depicted with textual description, which may severely restrict the interactivity of human and the chatbot, especially when the agent needs to answer the query excluded in the predefined personas, which is so-called out-of-predefined persona problem (named OOP for simplicity). To alleviate the problem, in this paper we propose a novel retrieval-to-prediction paradigm consisting of two subcomponents, namely, (1) Persona Retrieval Model (PRM), it retrieves a persona from a global collection based on a Natural Language Inference (NLI) model, the inferred persona is consistent with the predefined personas; and (2) Posterior-scored Transformer (PS-Transformer), it adopts a persona posterior distribution that further considers the actual personas used in the ground response, maximally mitigating the gap between training and inferring. Furthermore, we present a dataset called IT-ConvAI2 that first highlights the OOP problem in personalized dialogue. Extensive experiments on both IT-ConvAI2 and ConvAI2 demonstrate that our proposed model yields considerable improvements in both automatic metrics and human evaluations.
