Feb 1, 2021 · We can consider that by using the attention mechanism, there is this idea of freeing the existing encoder-decoder architecture from the fixed-short-length internal representation of text.

Mar 2, 2025 · Continuing our breakdown of the Transformer architecture and understanding it in detail, we now arrive at one of its most crucial components: the attention mechanism. In previous discussions,...

Aug 7, 2019 · In this tutorial, you will discover the attention mechanism for the Encoder-Decoder model. After completing this tutorial, you will know: About the Encoder-Decoder model and attention mechanism for machine translation. How to implement the attention mechanism step-by-step. Applications and extensions to the attention mechanism.

Nov 10, 2023 · Later, we’ll introduce a technique that has been a great step forward in the treatment of NLP tasks: the attention mechanism. We’ll detail a basic processing of the attention applied to a...

Mar 29, 2025 · The original transformer architecture is composed of an encoder and a decoder. Its layout is shown in the figure below. Recall that the transformer model was developed for translation tasks, replacing the seq2seq architecture that was commonly used with RNNs. Therefore, it borrowed the encoder-decoder architecture.

Apr 10, 2025 · After exploring traditional attention mechanisms in encoder-decoder architectures, we now shift focus to self-attention, a core component of modern models like the Transformer. Self-attention, also referred to as intra-attention, is a mechanism that allows each position in a sequence to attend to all other positions in the same sequence. This ...

Apr 11, 2025 · Cross-attention primarily serves to connect the Encoder and Decoder in an Encoder-Decoder architecture. It allows the decoder, as it generates output tokens, to continually refer back to the entire encoded input information via the K and V vectors from the encoder. ... The attention mechanism, specifically scaled dot-product self-attention, is ...

Nov 28, 2023 · An attention mechanism is an Encoder-Decoder kind of neural network architecture that allows the model to focus on specific sections of the input while executing a task. It dynamically assigns weights to different elements in the input, indicating their relative importance or relevance.

Jan 6, 2023 · Here, the attention mechanism (ϕ) learns a set of attention weights that capture the relationship between the encoded vectors (v) and the hidden state of the decoder (h) to generate a context vector (c) through a weighted sum of all the hidden states of the encoder.

4 days ago · A key feature of the proposed model is its bidirectional architecture, enabling it to capture contextual information in both forward and backward directions. ... To capture this incongruity and better contextual information, the proposed work incorporates various attention mechanisms with an encoder-decoder model such as Global , Bahdanau ...