title: ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation
    Extraction on an Academic Budget
summary: 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.
paper_authors._id: 66ac825ce5372209a0da2856
paper_authors.name: Riccardo Orlando
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-03T08:25:50.013Z
paper_authors._id: 5f0b462819cb630495b814d7
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paper_authors.fullname: Riccardo Orlando
paper_authors.type: user
paper_authors.user: riccorl
paper_authors._id: 66ac825ce5372209a0da2857
paper_authors.name: Pere-Lluis Huguet-Cabot
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-03T08:25:51.763Z
paper_authors._id: 5f04e7545d08220171a0ad3e
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paper_authors.fullname: Pere-Lluis Huguet Cabot
paper_authors.type: user
paper_authors.user: PereLluis13
paper_authors._id: 66ac825ce5372209a0da2858
paper_authors.name: Edoardo Barba
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-05T09:02:35.578Z
paper_authors._id: 614dc83d8d64cfc347c28e42
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paper_authors.fullname: Edoardo Barba
paper_authors.type: user
paper_authors.user: edobobo
paper_authors._id: 66ac825ce5372209a0da2859
paper_authors.name: Roberto Navigli
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-05T09:17:07.133Z
paper_authors._id: 63845c7c99292a80134bc784
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63845c7c99292a80134bc784/eKOPJMjm8F-JUKT_mi7Dv.png
paper_authors.fullname: Roberto Navigli
paper_authors.type: user
paper_authors.user: navigli
paper_title: ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation
    Extraction on an Academic Budget
paper_summary: 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.
paper_ai_summary: ReLiK, a Retriever-Reader architecture, achieves state-of-the-art performance in Entity Linking and Relation Extraction while significantly reducing inference speed.
paper_ai_keywords: Retriever Reader Entity Linking Relation Extraction input representation pre-trained language models contextualization in-domain out-of-domain Information Extraction shared Reader