A TensorFlow Implementation of Machine Translation In Neural Machine Translation in Linear Time
- numpy >= 1.11.1
- TensorFlow >= 1.1 (Probably 1.0 should work as well.)
- nltk >= 3.2.2 (only for calculating the bleu score)
- This implementation is different from the paper in the following aspects.
- I used the IWSLT 2016 de-en dataset, not the wmt 2014 de-en dataset, which is much bigger.
- I applied a greedy decoder at the inference phase, not the beam search decoder.
- I didn't implement
Dynamic Unfolding.
- STEP 1. Download IWSLT 2016 German–English parallel corpus and extract it to
corpora/folder. - STEP 2. Run
train.py. - STEP 3. Run
eval.pyto get test results
Or if you'd like to use the pretrained model,
- Download and extract the pre-trained model files, and then run
eval.py.
After 15 epochs, I obtained the Bleu score 7.38, which is far from good. Maybe some part in the implementation is incorrect. Or maybe we need more data or a bigger model. Details are available in the results folder.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent