Neural Machine Translation by Jointly Learning to Align and Translate
Attention mechanism for LSTMs! Mostly, figures and formulas and their explanations revealed to be useful to me. I gave a talk on that paper here.
- from
- Education
- added
- 2026-10-10
- likes
- 0
similar
-
Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation arxiv.org
In 2016: stacked residual LSTMs with attention mechanisms on encoder/decoder are the best for NMT (Neural Machine Translation).
-
Neural Machine Translation and Sequence-to-sequence Models: A Tutorial arxiv.org
Interesting overview of the subject of NMT, I mostly read part 8 about RNNs with attention as a refresher.
-
Effective Approaches to Attention-based Neural Machine Translation arxiv.org
Exploring different approaches to attention mechanisms.
-
Massive Exploration of Neural Machine Translation Architectures arxiv.org
That yields intuition about the boundaries of what works for doing NMT within a framed seq2seq problem formulation.
-
Machine Translation using RNN (Paper) emnlp2014.org
-
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention arxiv.org
LSTMs' attention mechanisms on CNNs feature maps does wonders.
Education › Papers > Attention Mechanisms: “Attention mechanism for LSTMs! Mostly, figures and formulas and their explanations revealed to be useful to me. I gave a talk on that paper here.”