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  3. ieeexplore.ieee.org › document › 9194070Attention in Natural Language Processing | IEEE Journals ...

    Sep 10, 2020 · 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 ...

    • Andrea Galassi, Marco Lippi, Paolo Torroni
    • 2021

  4. medium.com › analytics-vidhya › https-medium-comUnderstanding Attention Mechanism: Natural Language Processing

    Jan 27, 2020 · Attention is proposed as a solution to the limitation of the Encoder-Decoder model which encodes the input sequence to one fixed length vector from which to decode the output at each time step.

  5. www.datacamp.com › blog › attention-mechanism-inWhat is Attention and Why Do LLMs and Transformers Need It?

    Apr 26, 2024 · Natural language processing (NLP) models struggled for years to effectively capture the nuances of human language until a breakthrough happened — the attention mechanism. The attention mechanism was introduced in 2017 in the paper Attention Is All You Need.

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  7. slds-lmu.github.io › seminar_nlp_ss20 › attentionChapter 8 Attention and Self-Attention for NLP | Modern ...

    While Bahdanau, Cho, and Bengio were the first to use attention in neural machine translation, Luong, Pham, and Manning were the first to explore different attention mechanisms and their impact on NMT. Luong et al. also generalise the attention mechanism for the decoder which enables a quick switch between different attention functions.

  8. arxiv.org › abs › 1902[1902.02181] Attention in Natural Language Processing - arXiv.org

    Feb 4, 2019 · 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.

    • Andrea Galassi, Marco Lippi, Paolo Torroni
    • I.2; I.7
    • 2021
    • arXiv:1902.02181 [cs.CL]

  9. towardsdatascience.com › all-you-need-to-knowAll you need to know about ‘Attention’ and ‘Transformers ...

    Feb 14, 2022 · In 2015, attention was used first in Natural Language Processing (NLP) in Aligned Machine Translation. Finally, in 2017, the attention mechanism was used in Transformer networks for language modeling. Transformers have since surpassed the prediction accuracies of Recurrent Neural Networks (RNNs), to become state-of-the-art for NLP tasks. 1.

  10. arxiv.org › pdf › 1902Attention in Natural Language Processing - arXiv.org

    Attention was first introduced in natural language process-ing (NLP) for machine translation tasks by Bahdanau et al. [2].