Personalized response selection systems are generally grounded on persona. However, there exists a co-relation between persona and empathy, which is not explored well in these systems. Also, faithfulness to the conversation context plunges when a contradictory or an off-topic response is selected. This paper attempts to address these issues by proposing a suite of fusion strategies that capture the interaction between persona, emotion, and entailment information of the utterances. Ablation studies on the Persona-Chat dataset show that incorporating emotion and entailment improves the accuracy of response selection. We combine our fusion strategies and concept-flow encoding to train a BERT-based model which outperforms the previous methods by margins larger than 2.3 % on original personas and 1.9 % on revised personas in terms of hits@1 (top-1 accuracy), achieving a new state-of-the-art performance on the Persona-Chat dataset.
翻译:个人化响应选择系统一般以个人为基础。 然而, 个人化响应选择系统与共鸣之间存在一种相互关系, 这些系统对此没有很好地探讨。 此外, 当选择自相矛盾或非主题的反应时, 对对话背景的忠诚就会减弱。 本文试图通过提出一套集成战略来解决这些问题, 以捕捉个人、 情感和言论的必然信息之间的相互作用。 人与电数据集的模拟研究表明, 包含情感和必然因素会提高响应选择的准确性。 我们结合我们的聚合策略和概念流编码, 来训练一种基于 BERT 的模型, 该模型在原人身上以大于2.3%的边际, 在订正人之间以点击率大于1.9%的边际( 顶端-1 准确性), 实现个人- 帽子数据集的新状态 。