Attention in Natural Language Processing

Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats. However, because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a u...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 32; H. 10; S. 4291 - 4308
Hauptverfasser: Galassi, Andrea, Lippi, Marco, Torroni, Paolo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Computational modeling
Computer architecture
Distribution functions
Domains
Language
Natural language processing
Natural language processing (NLP)
neural attention
Neural networks
Representations
review
survey
Task analysis
Taxonomy
Visualization
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats. However, because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures in natural language processing, with a focus on those designed to work with vector representations of the textual data. We propose a taxonomy of attention models according to four dimensions: the representation of the input, the compatibility function, the distribution function, and the multiplicity of the input and/or output. We present the examples of how prior information can be exploited in attention models and discuss ongoing research efforts and open challenges in the area, providing the first extensive categorization of the vast body of literature in this exciting domain.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.3019893