论文标题
知识接地的对话框状态跟踪
Knowledge-grounded Dialog State Tracking
论文作者
论文摘要
知识（包括架构和本体论等结构化知识，以及诸如Web语料库之类的非结构化知识）是对话理解的关键部分，尤其是对于看不见的任务和域而言。传统上，这种特定领域的知识被隐式地编码为执行下游任务的模型参数，这使得训练效率低下。此外，此类模型不容易转移到具有不同模式的新任务中。在这项工作中，我们建议执行基于外部编码的知识的对话状态跟踪。我们根据对话框上下文查询各种形式的相关知识，在该上下文中，此类信息可以基于对话态的预测。我们证明了我们所提出的方法优于强大基线，尤其是在少数几个学习环境中。
Knowledge (including structured knowledge such as schema and ontology, and unstructured knowledge such as web corpus) is a critical part of dialog understanding, especially for unseen tasks and domains. Traditionally, such domain-specific knowledge is encoded implicitly into model parameters for the execution of downstream tasks, which makes training inefficient. In addition, such models are not easily transferable to new tasks with different schemas. In this work, we propose to perform dialog state tracking grounded on knowledge encoded externally. We query relevant knowledge of various forms based on the dialog context where such information can ground the prediction of dialog states. We demonstrate superior performance of our proposed method over strong baselines, especially in the few-shot learning setting.
