Massive Exploration of Neural Machine Translation Architectures
That yields intuition about the boundaries of what works for doing NMT within a framed seq2seq problem formulation.
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- 2026-10-10
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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.
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Neural Machine Translation jair.org
A paper-review that trace back the origins of modern NMT architectures to word and sentence embeddings and earlier examples of the encoder-decoder network family. It will conclude with a short survey of more recent trends in the field.
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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).
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Neural Machine Translation by Jointly Learning to Align and Translate arxiv.org
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.
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Effective Approaches to Attention-based Neural Machine Translation arxiv.org
Exploring different approaches to attention mechanisms.
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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation arxiv.org
Two networks in one combined into a seq2seq (sequence to sequence) Encoder-Decoder architecture. RNN Encoder–Decoder with 1000 hidden units. Adadelta optimizer.
Education › Papers > Attention Mechanisms: “That yields intuition about the boundaries of what works for doing NMT within a framed seq2seq problem formulation.”