ReLiK：检索和链接，基于学术预算快速准确的实体链接和关系抽取

ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget

July 31, 2024

作者: Riccardo Orlando, Pere-Lluis Huguet-Cabot, Edoardo Barba, Roberto Navigli

cs.AI

摘要

实体链接（EL）和关系抽取（RE）是自然语言处理中的基本任务，在各种应用中扮演关键角色。本文提出了ReLiK，一种适用于EL和RE的检索器-阅读器架构，在给定输入文本的情况下，检索器模块负责识别可能出现在文本中的候选实体或关系。随后，阅读器模块的任务是辨别相关的检索实体或关系，并建立它们与相应文本跨度的对齐。值得注意的是，我们提出了一种创新的输入表示，将候选实体或关系与文本一起整合，使得能够在单次前向传递中链接实体或提取关系，并充分利用预训练语言模型的上下文化能力，与之前的基于检索器-阅读器的方法形成对比，后者需要为每个候选实体进行一次前向传递。我们的EL和RE配方在领域内外基准测试中实现了最先进的性能，同时使用学术预算训练，并与竞争对手相比推理速度提高了多达40倍。最后，我们展示了我们的架构如何无缝地用于信息提取（cIE），即EL + RE，并通过使用共享阅读器同时提取实体和关系，树立了一个新的技术水平。

English

Entity Linking (EL) and Relation Extraction (RE) are fundamental tasks in Natural Language Processing, serving as critical components in a wide range of applications. In this paper, we propose ReLiK, a Retriever-Reader architecture for both EL and RE, where, given an input text, the Retriever module undertakes the identification of candidate entities or relations that could potentially appear within the text. Subsequently, the Reader module is tasked to discern the pertinent retrieved entities or relations and establish their alignment with the corresponding textual spans. Notably, we put forward an innovative input representation that incorporates the candidate entities or relations alongside the text, making it possible to link entities or extract relations in a single forward pass and to fully leverage pre-trained language models contextualization capabilities, in contrast with previous Retriever-Reader-based methods, which require a forward pass for each candidate. Our formulation of EL and RE achieves state-of-the-art performance in both in-domain and out-of-domain benchmarks while using academic budget training and with up to 40x inference speed compared to competitors. Finally, we show how our architecture can be used seamlessly for Information Extraction (cIE), i.e. EL + RE, and setting a new state of the art by employing a shared Reader that simultaneously extracts entities and relations.