Extracting structured information from unstructured text is critical for many
downstream NLP applications and is traditionally achieved by closed information
extraction (cIE). However, existing approaches for cIE suffer from two
limitations: (i) they are often pipelines which makes them prone to error
propagation, and/or (ii) they are restricted to sentence level which prevents
them from capturing long-range dependencies and results in expensive inference
time. We address these limitations by proposing REXEL, a highly efficient and
accurate model for the joint task of document level cIE (DocIE). REXEL performs
mention detection, entity typing, entity disambiguation, coreference resolution
and document-level relation classification in a single forward pass to yield
facts fully linked to a reference knowledge graph. It is on average 11 times
faster than competitive existing approaches in a similar setting and performs
competitively both when optimised for any of the individual subtasks and a
variety of combinations of different joint tasks, surpassing the baselines by
an average of more than 6 F1 points. The combination of speed and accuracy
makes REXEL an accurate cost-efficient system for extracting structured
information at web-scale. We also release an extension of the DocRED dataset to
enable benchmarking of future work on DocIE, which is available at
this https URL

REXEL 是一种高效准确的模型，可以在单个前向传递中执行提及检测、实体类型识别、实体消歧、共指消解和文档级关系分类等多个任务，将事实完全链接到参考知识图谱，具有速度和准确性的组合使得 REXEL 成为一种准确且具有成本效益的用于在 Web 规模上提取结构化信息的系统。

REXEL：面向文档级关系抽取和实体链接的端到端模型

REXEL: An End-to-end Model for Document-Level Relation Extraction and  Entity Linking

Large language models (LLMs) show great potential for synthetic data
generation. This work shows that useful data can be synthetically generated
even for tasks that cannot be solved directly by the LLM: we show that, for
problems with structured outputs, it is possible to prompt an LLM to perform
the task in the opposite direction, to generate plausible text for the target
structure. Leveraging the asymmetry in task difficulty makes it possible to
produce large-scale, high-quality data for complex tasks. We demonstrate the
effectiveness of this approach on closed information extraction, where
collecting ground-truth data is challenging, and no satisfactory dataset exists
to date. We synthetically generate a dataset of 1.8M data points, demonstrate
its superior quality compared to existing datasets in a human evaluation and
use it to finetune small models (220M and 770M parameters). The models we
introduce, SynthIE, outperform existing baselines of comparable size with a
substantial gap of 57 and 79 absolute points in micro and macro F1,
respectively. Code, data, and models are available at
this https URL.

本研究展示了大型语言模型的潜力，即使在无法直接解决 LLM 的任务中，也可以合成有用的数据。其中一项方法是通过引导 LLM 生成符合目标结构的文本，此方法已经成功应用于闭合信息提取，并通过 SynthIE 模型在微观和宏观 F1 值上超过了同样规模的基准模型 57 和 79 个百分点。

利用不对称性进行合成训练数据生成：SynthIE 与信息抽取案例

Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction

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。