论文标题
盗版：用自我监督的命名实体消除歧义来追逐尾巴
Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation
论文作者
论文摘要
对命名实体歧义（NED）的挑战是，将文本提及映射给知识库中的实体的任务是如何消除很少出现在培训数据中的实体，称为尾巴实体。人类使用基于对实体事实，关系和类型的知识的微妙推理模式来消除陌生的实体。受这些模式的启发，我们引入了Bootleg，这是一种自我监督的NED系统，该系统明确地基于歧义的推理模式。我们为歧义定义了核心推理模式，创建一个学习程序来鼓励自学模型学习模式，并展示如何使用弱监督来增强培训数据中的信号。在简单的变压器体系结构中编码推理模式，Bootleg满足或超过了三个NED基准测试的最先进。我们进一步表明，从盗版中学到的熟悉的表示形式成功地转移到需要基于实体知识的其他非分解任务：我们将流行的Tacred关系提取任务中的新最新设置为1.0 F1点，并在高度优化的主要技术公司的高度优化生产和助理任务中表现出多达8％的性能提升
A challenge for named entity disambiguation (NED), the task of mapping textual mentions to entities in a knowledge base, is how to disambiguate entities that appear rarely in the training data, termed tail entities. Humans use subtle reasoning patterns based on knowledge of entity facts, relations, and types to disambiguate unfamiliar entities. Inspired by these patterns, we introduce Bootleg, a self-supervised NED system that is explicitly grounded in reasoning patterns for disambiguation. We define core reasoning patterns for disambiguation, create a learning procedure to encourage the self-supervised model to learn the patterns, and show how to use weak supervision to enhance the signals in the training data. Encoding the reasoning patterns in a simple Transformer architecture, Bootleg meets or exceeds state-of-the-art on three NED benchmarks. We further show that the learned representations from Bootleg successfully transfer to other non-disambiguation tasks that require entity-based knowledge: we set a new state-of-the-art in the popular TACRED relation extraction task by 1.0 F1 points and demonstrate up to 8% performance lift in highly optimized production search and assistant tasks at a major technology company
