Pytorch-seq2seq-Beam-Search

Seq2Seq model with attention and Greedy Search / Beam Search for neural machine translation in PyTorch.

This implementation focuses on the following features:

• Modular structure to be used in other projects
• Minimal code for readability
• Full utilization of batches and GPU.
• Decoding Method Greedy Search
• Decoding Method Beam Search

This implementation relies on torchtext to minimize dataset management and preprocessing parts.

Seq2Seq Model description

The main structure of seq2seq is adopt in seq2seq

• Encoder: Bidirectional GRU
• Decoder: GRU with Attention Mechanism
• Attention

Decoding Method

• Greedy Search
• Beam Search

Requirements

• CUDA
• Python 3.6
• PyTorch 1.4
• torchtext
• Spacy
• numpy
• Visdom (optional)

download tokenizers by doing so:

python -m spacy download de
python -m spacy download en

TODO

1. add logging
2. change to support gpu and cpu, currently is gpu based code implementation
3. Do Length normalization experiment on Beam-Search And Compare

References

Based on the following implementations

• Neural Machine Translation by Jointly Learning to Align and Translate
• seq2seq
• PyTorch-Beam-Search-Decoding
• Conditional Generation by RNN & Attention
• cs224n-2019-notes06-NMT_seq2seq_attention