# Fine-Grained Attention Mechanism for Neural Machine Translation

arXiv: https://arxiv.org/abs/1803.11407 · HTML: https://mlsanity.com/paper/1803.11407/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks](https://mlsanity.com/paper/2101.10642/) (2021)
- [Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains](https://mlsanity.com/paper/2109.14276/) (2021)
- [From No to Know: Taxonomy, Challenges, and Opportunities for Negation Understanding in Multimodal Foundation Models](https://mlsanity.com/paper/2502.09645/) (2025)

## Abstract (arXiv)

Neural machine translation (NMT) has been a new paradigm in machine translation, and the attention mechanism has become the dominant approach with the state-of-the-art records in many language pairs. While there are variants of the attention mechanism, all of them use only temporal attention where one scalar value is assigned to one context vector corresponding to a source word. In this paper, we propose a fine-grained (or 2D) attention mechanism where each dimension of a context vector will receive a separate attention score. In experiments with the task of En-De and En-Fi translation, the fine-grained attention method improves the translation quality in terms of BLEU score. In addition, our alignment analysis reveals how the fine-grained attention mechanism exploits the internal structure of context vectors.