ReLiK：檢索和連結，快速準確的實體鏈接和關係提取在學術預算下

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

實體鏈接（EL）和關係提取（RE）是自然語言處理中的基本任務，在各種應用中扮演著重要角色。本文提出了一種名為ReLiK的Retriever-Reader架構，用於EL和RE，其中，給定一個輸入文本，檢索器模組負責識別可能出現在文本中的候選實體或關係。隨後，閱讀器模組的任務是辨別相關的檢索實體或關係，並確立它們與相應文本範圍的對齊。值得注意的是，我們提出了一種創新的輸入表示，將候選實體或關係與文本一起納入，使得能夠在單次前向傳遞中鏈接實體或提取關係，並充分利用預訓練語言模型的情境化能力，與之前的Retriever-Reader方法相比，後者需要對每個候選進行前向傳遞。我們的EL和RE公式在領域內外基準測試中實現了最先進的性能，同時使用學術預算訓練，並與競爭對手相比，推理速度最多提高了40倍。最後，我們展示了如何無縫地使用我們的架構進行信息提取（cIE），即EL + RE，並通過使用共享的閱讀器同時提取實體和關係，創立了一個新的技術水平。

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.