ramkishore07s / Neural-Machine-Translation Public

Notifications You must be signed in to change notification settings
Fork 0
Star 0

Implementation of various Neural Machine Translation papers

0 stars 0 forks Branches Tags Activity

Notifications

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
data		data
images		images
.gitignore		.gitignore
README.md		README.md
coverage.ipynb		coverage.ipynb
effective_approaches.ipynb		effective_approaches.ipynb
main.ipynb		main.ipynb
seq2seq.ipynb		seq2seq.ipynb
seq2seq_with_attention.ipynb		seq2seq_with_attention.ipynb
train_and_test.ipynb		train_and_test.ipynb
utils.ipynb		utils.ipynb

Repository files navigation

Neural-Machine-Translation

Running the models:

Run train_and_test.ipynb to train and test all models
To use them on command line, do:
- jupyter nbconvert --to=script train_and_test.ipynb
- ipython train_and_test.py

Architecture Diagrams:

seq2seq: Sequence to Sequence Learning with Neural Networks
attention: Neural Machine Translation By Jointly Learning To Align And Translate
effective approaches: Effective Approaches to Attention-based Neural Machine Translation
coverage: Modeling Coverage for Neural Machine Translation

Results obtained on the sample dataset (english -> tamil):

Click on the model name to download trained models

Model (Seq2Seq)	Bleu-Score
Linguistic coverage	0.089
General attention	0.087
Fertility coverage	0.0829
Vanilla	0.082
Concat attention	0.0814
Dot attention	0.079
MLP attention	0.072