Information extraction (IE) aims to extract complex structured information
from the text. Numerous datasets have been constructed for various IE tasks,
leading to time-consuming and labor-intensive data annotations. Nevertheless,
most prevailing methods focus on training task-specific models, while the
common knowledge among different IE tasks is not explicitly modeled. Moreover,
the same phrase may have inconsistent labels in different tasks, which poses a
big challenge for knowledge transfer using a unified model. In this study, we
propose a regularization-based transfer learning method for IE (TIE) via an
instructed graph decoder. Specifically, we first construct an instruction pool
for datasets from all well-known IE tasks, and then present an instructed graph
decoder, which decodes various complex structures into a graph uniformly based
on corresponding instructions. In this way, the common knowledge shared with
existing datasets can be learned and transferred to a new dataset with new
labels. Furthermore, to alleviate the label inconsistency problem among various
IE tasks, we introduce a task-specific regularization strategy, which does not
update the gradients of two tasks with 'opposite direction'. We conduct
extensive experiments on 12 datasets spanning four IE tasks, and the results
demonstrate the great advantages of our proposed method

本研究提出了一种基于正则化的迁移学习方法，通过指导图解码器进行信息提取（IE）。我们构建了一个指令池，用于各种 IE 任务的数据集，然后提出了一个指导图解码器。通过基于相应指令将各种复杂结构解码为图的方式，我们可以学习和转移与现有数据集共享的常见知识到具有新标签的新数据集中。此外，为了缓解各种 IE 任务中的标签一致性问题，我们引入了一种任务特定的正则化策略，不更新具有 “相反方向” 的两个任务的梯度。我们对包括四个 IE 任务的 12 个数据集进行了大量实验，结果证明了我们提出方法的巨大优势。

基于正则化的迁移学习方法在通过指导的图解码器中的信息提取

A Regularization-based Transfer Learning Method for Information  Extraction via Instructed Graph Decoder

Information Extraction (IE) aims to extract structural knowledge (e.g.,
entities, relations, events) from natural language texts, which brings
challenges to existing methods due to task-specific schemas and complex text
expressions. Code, as a typical kind of formalized language, is capable of
describing structural knowledge under various schemas in a universal way. On
the other hand, Large Language Models (LLMs) trained on both codes and texts
have demonstrated powerful capabilities of transforming texts into codes, which
provides a feasible solution to IE tasks. Therefore, in this paper, we propose
a universal retrieval-augmented code generation framework based on LLMs, called
Code4UIE, for IE tasks. Specifically, Code4UIE adopts Python classes to define
task-specific schemas of various structural knowledge in a universal way. By so
doing, extracting knowledge under these schemas can be transformed into
generating codes that instantiate the predefined Python classes with the
information in texts. To generate these codes more precisely, Code4UIE adopts
the in-context learning mechanism to instruct LLMs with examples. In order to
obtain appropriate examples for different tasks, Code4UIE explores several
example retrieval strategies, which can retrieve examples semantically similar
to the given texts. Extensive experiments on five representative IE tasks
across nine datasets demonstrate the effectiveness of the Code4UIE framework.

Code4UIE 是基于 LLMs 的一种通用检索增强代码生成框架，针对信息抽取任务。它采用 Python 类来以通用方式定义各种结构化知识的任务特定模式，并且利用上下文学习机制指导 LLMs 生成恰当的代码，以从文本中提取知识。Code4UIE 通过多种示例检索策略探索语义上与给定文本相似的示例，通过五个代表性的信息抽取任务在九个数据集上的广泛实验验证了该框架的有效性。

检索增强的通用信息提取代码生成

Retrieval-Augmented Code Generation for Universal Information Extraction

Extracting information from full documents is an important problem in many
domains, but most previous work focus on identifying relationships within a
sentence or a paragraph. It is challenging to create a large-scale information
extraction (IE) dataset at the document level since it requires an
understanding of the whole document to annotate entities and their
document-level relationships that usually span beyond sentences or even
sections. In this paper, we introduce SciREX, a document level IE dataset that
encompasses multiple IE tasks, including salient entity identification and
document level $N$-ary relation identification from scientific articles. We
annotate our dataset by integrating automatic and human annotations, leveraging
existing scientific knowledge resources. We develop a neural model as a strong
baseline that extends previous state-of-the-art IE models to document-level IE.
Analyzing the model performance shows a significant gap between human
performance and current baselines, inviting the community to use our dataset as
a challenge to develop document-level IE models. Our data and code are publicly
available at this https URL

本文介绍 SciREX，它是一个涵盖多个 IE 任务的文档级 IE 数据集，包括通过科学文章识别突出实体和文档级的 $n$ 元关系识别。我们开发了一个神经模型作为一个强有力的基线，扩展了先前的 IE 模型到文档级 IE。分析模型性能显示，人类表现和当前基线之间存在显著差距，邀请社区使用我们的数据集作为发展文档级 IE 模型的挑战。