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tensorflow-shakespeare

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Neural machine translation between the writings of Shakespeare and modern English using TensorFlow

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# Shakespeare translations using TensorFlow

This is an example of using the new Google's [TensorFlow](https://github.com/tensorflow/tensorflow) library on
monolingual translation going from modern English to Shakespeare based on research from 
[Wei Xu](https://github.com/cocoxu/Shakespeare).

## Prepare

First download the TensorFlow library depending on your platform:

```
pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl # for mac
pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl # for ubuntu
```

1. Grabs parallel data.
2. Gets train, dev split.
3. Builds vocabulary
4. Converts parallel data into ids

From the root directory:

```
python -m tensorshake.get_data
python -m tensorshake.prepare_corpus
```

Delete /cache to start anew.

## Train

Use the example BASH script to train the model. This saves the check points in the `--train_dir` directory.
If you run it again, the training process continues from the check point. To restart with fresh parameters,
simply delete/rename the check points.

```
./run.sh
```

## Results

[Benchmarks from original paper.](http://aclweb.org/anthology/C/C12/C12-1177.pdf) (Shakespeare -> Modern English)

Input | Output
 --- | ---
i will bite thee by the ear for that jest . | i ’ ll bite you by the ear for that joke .
what further woe conspires against mine age ? | what ’ s true despair conspires against my old age ?
how doth my lady ? |how is my lady ?
hast thou slain tybalt ? |have you killed tybalt ?
an i might live to see thee married once , i have my wish .| if i could live to see you married, i ’ ve my wish .
benvolio , who began this bloody fray ? | benvolio , who started this bloody fight itself ?
what is your will ? | what do you want ? 
call her forth to me . |bring her out to me .

*Cherrypicked examples from this repo* (Modern English -> Shakespeare)

| Input | Output 
----- | ---
but you’re not listening to me. | but you do not hear me .
Gregory, on my word, we will not be humiliated, like carrying coal. | regory , we 'll not carry coals .
but he got the promotion.  | he is the friend .
i can hit quickly, if i'm motivated. | i strike , i am moved .
Did you just give us the finger, sir? | have you leave the thumb , sir ?
You don’t know what you’re doing! | you do not what you know you .
have you killed Tybalt? | hast thou slain tybalt ?
Why, Romeo, are you crazy? | why , art thou mad , mad ?

## Pre-Trained Models

Here is a link for an example model: https://s3-us-west-2.amazonaws.com/foxtype-nlp/tensorshake/model_cache.zip

## Possible improvements

- word embeddings
- beam search
- language model reranking

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