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Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText. - pytorch-seq2seq/2 - Learning Phrase Representations ...

The paper proposes a new RNN Encoder-Decoder architecture that can improve the performance of statistical machine translation (SMT) systems. Link to the paper ...

Jun 3, 2014 ， In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN).

This model will be based off an implementation of Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, which uses ...

PyTorch implementation of recurrent neural network encoder-decoder architecture model for statistical machine translation, as detailed in this paper.

Jun 3, 2014 ， In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN).

Missing: github | Show results with:github

Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation ... Submit results from this paper to get state-of-the-art GitHub ...

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre.

A minimal PyTorch implementation of RNN Encoder-Decoder for sequence to sequence learning. Supported features: Mini-batch training with CUDA.

Qualitatively, the proposed RNN Encoder‐Decoder model learns a semantically and syntactically meaningful representation of linguistic phrases. In this paper ...

Missing: github | Show results with:github

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