Structured and grounded representation of text is typically formalized by
closed information extraction, the problem of extracting an exhaustive set of
(subject, relation, object) triplets that are consistent with a predefined set
of entities and relations from a knowledge base schema. Most existing works are
pipelines prone to error accumulation, and all approaches are only applicable
to unrealistically small numbers of entities and relations. We introduce GenIE
(generative information extraction), the first end-to-end autoregressive
formulation of closed information extraction. GenIE naturally exploits the
language knowledge from the pre-trained transformer by autoregressively
generating relations and entities in textual form. Thanks to a new bi-level
constrained generation strategy, only triplets consistent with the predefined
knowledge base schema are produced. Our experiments show that GenIE is
state-of-the-art on closed information extraction, generalizes from fewer
training data points than baselines, and scales to a previously unmanageable
number of entities and relations. With this work, closed information extraction
becomes practical in realistic scenarios, providing new opportunities for
downstream tasks. Finally, this work paves the way towards a unified end-to-end
approach to the core tasks of information extraction. Code, data and models
available at this https URL

本文介绍了 GenIE 技术，这是第一个可应用于现实场景的 autoregressive formulation 方法，通过生成文本形式的关系和实体，仅生成与预定义知识库模式一致的三元组。实验表明，GenIE 在关闭信息提取方面表现最佳，并且可以推广到相对较少的训练数据点，尺度可以扩展到以前难于管理的实体和关系数量。

GenIE: 生成式信息提取

GenIE: Generative Information Extraction

Existing entity typing systems usually exploit the type hierarchy provided by
knowledge base (KB) schema to model label correlations and thus improve the
overall performance. Such techniques, however, are not directly applicable to
more open and practical scenarios where the type set is not restricted by KB
schema and includes a vast number of free-form types. To model the underly-ing
label correlations without access to manually annotated label structures, we
introduce a novel label-relational inductive bias, represented by a graph
propagation layer that effectively encodes both global label co-occurrence
statistics and word-level similarities.On a large dataset with over 10,000
free-form types, the graph-enhanced model equipped with an attention-based
matching module is able to achieve a much higher recall score while maintaining
a high-level precision. Specifically, it achieves a 15.3% relative F1
improvement and also less inconsistency in the outputs. We further show that a
simple modification of our proposed graph layer can also improve the
performance on a conventional and widely-tested dataset that only includes
KB-schema types.

本文提出了一种新的标签关系归纳偏好，利用图传播层有效编码全局标签共现统计和单词级别相似性，结合基于注意力的匹配模块，可以在包括超过 10,000 种自由形式类型的大型数据集上实现更高的召回分数，并且在输出的稳定性方面也更少不一致性。