When learning a second language, one of the hardest challenges is likely to be familiar with the word order. Word order can be also important in machine translation because translation is, roughly speaking, a process that one arranges words of target language which are equivalent to source language in order. Probably you've done a word scramble game where you are to put shuffled words or letters in the original order. I think it's quite fun to see if neural networks can do it. Okay. Can you order the following words correctly?

can translation machine order also important be word in

• NumPy >= 1.11.1
• TensorFlow == 1.2 (Probably 1.3 should work, too, though I didn't test it)
• matplotlib
• distance
• tqdm

I employ the Transformer which was introduced in the paper Attention Is All You Need. It's known to the state-of-the-art model in the machine translation task as of 2017. However, I don't know if it fits in this task the best. Actually, I think a simpler architecture may work. The figure below is borrowed from the paper.

• hyperparams.py includes all hyper parameters that are needed.
• data_load.py contains functions regarding loading and batching data.
• modules.py has all building blocks for encoder/decoder networks.
• train.py contains the model and training code.
• eval.py is for evaluation and inference.

• STEP 1. Download and extract Leipzig English News 2015 1M Corpus.
• STEP 2. Adjust hyper parameters in hyperparams.py if necessary.
• STEP 3. Run train.py or download the pretrained files.

• Training Loss

• Training Accuracy

• Run eval.py.

We take WER (Word Error Rate) as the metric. WER is computed as follows:

WER = Edit distance / Number of words

Total WER : 10731/23541=0.46

Check the results folder for details.