论文标题
人格知识对话多上下文检索和增强的解码方法
Persona-Knowledge Dialogue Multi-Context Retrieval and Enhanced Decoding Methods
论文作者
论文摘要
角色和知识双层上下文开放域聊天是最近介绍的一项新颖的对话生成任务。尽管角色和知识都是开放域对话的每个有趣的背景，但两者的结合尚未得到很好的研究。我们在本文中处理角色知识识别和响应生成任务。我们设计了一个知情的数据增强策略，该策略与神经Q＆A检索模型兼容。通过增强数据，我们执行置换性角色知识评估和连续的角色搜索微调。此外，我们通过各种解码技术进行对话生成，并说明了关键要素。我们在官方指标中实现SOTA，平均基精度为93.99％，Sacrebleu得分为23.62。
Persona and Knowledge dual context open-domain chat is a novel dialogue generation task introduced recently. While Persona and Knowledge is each interesting context of open-domain dialogue, the combination of both has not been well studied. We tackle Persona-Knowledge identification and response generation tasks in this paper. We design an informed data augmentation strategy that is compatible with neural Q&A retrieval models. With the augmented data, we perform permutative Persona-Knowledge evaluation and successive Persona search fine-tuning. Furthermore, we perform dialogue generation with various decoding techniques and illustrate crucial elements. We achieve SOTA across official metrics with 93.99% Grounding accuracy average and 23.62 SacreBLEU score.
