ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation
  Extraction on an Academic Budget
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.
66ac825ce5372209a0da2856
Riccardo Orlando
claimed_verified
2024-08-03T08:25:50.013Z
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Riccardo Orlando
user
riccorl
66ac825ce5372209a0da2857
Pere-Lluis Huguet-Cabot
claimed_verified
2024-08-03T08:25:51.763Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/5f04e7545d08220171a0ad3e/M0dWlakxnLakQlZl5iOq6.jpeg
Pere-Lluis Huguet Cabot
user
PereLluis13
66ac825ce5372209a0da2858
Edoardo Barba
claimed_verified
2024-08-05T09:02:35.578Z
614dc83d8d64cfc347c28e42
https://cdn-avatars.huggingface.co/v1/production/uploads/614dc83d8d64cfc347c28e42/FM3R1AHeO1I7P5gA3GD_V.jpeg
Edoardo Barba
user
edobobo
66ac825ce5372209a0da2859
Roberto Navigli
admin_assigned
2024-08-05T09:17:07.133Z
63845c7c99292a80134bc784
https://cdn-avatars.huggingface.co/v1/production/uploads/63845c7c99292a80134bc784/eKOPJMjm8F-JUKT_mi7Dv.png
Roberto Navigli
user
navigli
ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation
  Extraction on an Academic Budget
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.
ReLiK, a Retriever-Reader architecture, achieves state-of-the-art performance in Entity Linking and Relation Extraction while significantly reducing inference speed.
Retriever Reader Entity Linking Relation Extraction input representation pre-trained language models contextualization in-domain out-of-domain Information Extraction shared Reader