en2zh_translation

A Seq2Seq model with attention 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.

This implementation also use BLEU, NIST score and TER to evaluate the effect of the model.

Model description

• Encoder: Bidirectional GRU
• Decoder: GRU with Attention Mechanism
• Attention: Neural Machine Translation by Jointly Learning to Align and Translate

Metric

• BLUE
• NIST
• TER
• METEOR

perl mteval-v13a.pl -r example/ref.xml -s example/src.xml -t example/tst.xml

Use xml_transform.py to transorm sentence txt to xml file.

reference: https://blog.csdn.net/angus_monroe/article/details/82943162

Requirements

• GPU & CUDA
• Python3
• PyTorch
• torchtext
• Spacy
• numpy
• Visdom (optional)

References

Based on the following implementations

• PyTorch Tutorial
• @spro/practical-pytorch
• @AuCson/PyTorch-Batch-Attention-Seq2seq