论文标题
人工制品检索：具有知识库访问的NLP模型的概述
Artefact Retrieval: Overview of NLP Models with Knowledge Base Access
论文作者
论文摘要
许多NLP模型通过访问知识库来提高性能。许多研究致力于设计和改善知识库的访问和纳入模型的方式，从而产生了许多机制和管道。尽管提出的机制有多种多样，但此类系统的设计仍存在模式。在本文中，我们系统地描述了人工制品的类型（从知识库中检索的项目），检索机制以及这些伪像融合到模型中的方式。这进一步使我们能够发现尚未尝试的设计决策组合。大多数重点都放在语言模型上，尽管我们还展示了问题的回答，事实检查和知识渊博的对话模型也适合该系统。拥有一个可以描述特定模型体系结构的抽象模型也有助于在多个NLP任务之间传输这些体系结构。
Many NLP models gain performance by having access to a knowledge base. A lot of research has been devoted to devising and improving the way the knowledge base is accessed and incorporated into the model, resulting in a number of mechanisms and pipelines. Despite the diversity of proposed mechanisms, there are patterns in the designs of such systems. In this paper, we systematically describe the typology of artefacts (items retrieved from a knowledge base), retrieval mechanisms and the way these artefacts are fused into the model. This further allows us to uncover combinations of design decisions that had not yet been tried. Most of the focus is given to language models, though we also show how question answering, fact-checking and knowledgable dialogue models fit into this system as well. Having an abstract model which can describe the architecture of specific models also helps with transferring these architectures between multiple NLP tasks.
