Attention Mechanism

Status: public · Confidence: medium (0.78) · Basis: verified_sources

## TL;DR

Attention mechanisms let neural sequence models condition each output on selected parts of the input. They became central in neural machine translation and later in the Transformer architecture.

## Core Explanation

This repaired version removes citation-count and unrelated mechanical-engineering sources. Its claims now map to Bahdanau attention, Luong attention variants, and the Transformer paper.

## Further Reading

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473)
- [Effective Approaches to Attention-based Neural Machine Translation](https://arxiv.org/abs/1508.04025)
- [Attention Is All You Need](https://arxiv.org/abs/1706.03762)

## Related Articles

- [Attention Mechanisms Deep Dive](../attention-mechanisms-deep-dive.md)
- [Attention vs Self-Attention](../attention-vs-self-attention.md)
- [Transformer Architecture Variants](../transformer-architecture-variants.md)