Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes. In this work, we introduce the tasks of extracting and inferring personal attributes from human-human dialogue. We first demonstrate the benefit of incorporating personal attributes in a social chit-chat dialogue model and task-oriented dialogue setting. Thus motivated, we propose the tasks of personal attribute extraction and inference, and then analyze the linguistic demands of these tasks. To meet these challenges, we introduce a simple and extensible model that combines an autoregressive language model utilizing constrained attribute generation with a discriminative reranker. Our model outperforms strong baselines on extracting personal attributes as well as inferring personal attributes that are not contained verbatim in utterances and instead requires commonsense reasoning and lexical inferences, which occur frequently in everyday conversation.
翻译:个人属性代表一个人的结构性信息,如他们的爱好、宠物、家庭、喜欢和不喜欢。在这项工作中,我们引入了从人与人的对话中提取和推断个人属性的任务。我们首先展示了将个人属性纳入社会聊天对话模式和面向任务的对话环境的好处。因此,我们提出了个人属性提取和推断的任务,然后分析了这些任务的语言要求。为了应对这些挑战,我们引入了一个简单和可扩展的模式,将利用受限制的属性生成与歧视重排的自制语言模式结合起来。我们的模型超越了提取个人属性以及推断个人属性的强大基线,这些个人属性并非逐字不译地包含在言语中,而是需要常在日常谈话中常见的常识性推理和词汇推理。