基于事件因果性和角色因子张量事件嵌入的会话反应重排序
Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding
摘要：
我们提出了一种新的方法来选择一个给定的对话上下文的连贯性和多样性的反应。所提出的方法通过使用对话历史中的事件与响应候选之间的事件因果关系（例如，"be stressed out“（强调）先于”relieve stress“（减轻压力）。我们使用分布式事件表示的基础上的角色因子张量模型的事件因果关系，由于有限的事件因果关系的知识系统的一个强大的匹配。实验结果表明，该方法提高了系统响应的一致性和对话的连续性。
英文摘要：
We propose a novel method for selecting coherent and diverse responses for a given dialogue context. The proposed method re-ranks response candidates generated from conversational models by using event causality relations between events in a dialogue history and response candidates (e.g., ``be stressed out'' precedes ``relieve stress''). We use distributed event representation based on the Role Factored Tensor Model for a robust matching of event causality relations due to limited event causality knowledge of the system. Experimental results showed that the proposed method improved coherency and dialogue continuity of system responses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
