Update README with interactive.py and fix it

* `python preprocess.py`: Data pre-processing: build vocabularies and binarize training data

* `python train.py`: Train a new model on one or multiple GPUs

* `python generate.py`: Translate pre-processed data with a trained model

* `python generate.py -i`: Translate raw text with a trained model

* `python interactive.py`: Translate raw text with a trained model

* `python score.py`: BLEU scoring of generated translations against reference translations

## Evaluating Pre-trained Models

`@@` is used as a continuation marker and the original text can be easily recovered with e.g. `sed s/@@ //g` or by passing the `--remove-bpe` flag to `generate.py`.

Prior to BPE, input text needs to be tokenized using `tokenizer.perl` from [mosesdecoder](https://github.com/moses-smt/mosesdecoder).

Let's use `python generate.py -i` to generate translations.

Let's use `python interactive.py` to generate translations interactively.

Here, we use a beam size of 5:

```

$ MODEL_DIR=wmt14.en-fr.fconv-py

$ python generate.py -i \

$ python interactive.py \

 --path $MODEL_DIR/model.pt $MODEL_DIR \

 --beam 5

| loading model(s) from wmt14.en-fr.fconv-py/model.pt

| [en] dictionary: 44206 types

| [fr] dictionary: 44463 types

| model fconv_wmt_en_fr

| loaded checkpoint /private/home/edunov/wmt14.en-fr.fconv-py/model.pt (epoch 37)

| Type the input sentence and press return:

> Why is it rare to discover new marine mam@@ mal species ?

S       Why is it rare to discover new marine mam@@ mal species ?

O       Why is it rare to discover new marine mam@@ mal species ?

H       -0.08662842959165573    Pourquoi est-il rare de découvrir de nouvelles espèces de mammifères marins ?

A       0 1 3 3 5 6 6 10 8 8 8 11 12

H       -0.06429661810398102    Pourquoi est-il rare de découvrir de nouvelles espèces de mammifères marins ?

A       0 1 3 3 5 6 6 8 8 8 7 11 12

```

This generation script produces four types of outputs: a line prefixed with *S* shows the supplied source sentence after applying the vocabulary; *O* is a copy of the original source sentence; *H* is the hypothesis along with an average log-likelihood; and *A* is the attention maxima for each word in the hypothesis, including the end-of-sentence marker which is omitted from the text.

You may need to use a smaller value depending on the available GPU memory on your system.

### Generation

Once your model is trained, you can generate translations using `python generate.py` **(for binarized data)** or `python generate.py -i` **(for raw text)**:

Once your model is trained, you can generate translations using `python generate.py` **(for binarized data)** or `python interactive.py` **(for raw text)**:

```

$ python generate.py data-bin/iwslt14.tokenized.de-en \

  --path checkpoints/fconv/checkpoint_best.pt \

                    remove_bpe=args.remove_bpe)

                if not args.quiet:

                    print('A-{}\t{}'.format(sample_id, ' '.join(map(str, alignment))))

                    print('H-{}\t{}\t{}'.format(sample_id, hypo['score'], hypo_str))

                    print('A-{}\t{}'.format(sample_id, ' '.join(map(str, alignment))))

                # Score only the top hypothesis

                if i == 0:

    # (None if no unknown word replacement, empty if no path to align dictionary)

    align_dict = utils.load_align_dict(args.replace_unk)

    print('Type the input sentence and press return:')

    print('| Type the input sentence and press return:')

    for src_str in sys.stdin:

        src_str = src_str.strip()

        src_tokens = tokenizer.Tokenizer.tokenize(src_str, src_dict, add_if_not_exist=False).long()

                align_dict=align_dict,

                dst_dict=dst_dict,

                remove_bpe=args.remove_bpe)

            print('A\t{}'.format(' '.join(map(str, alignment))))

            print('H\t{}\t{}'.format(hypo['score'], hypo_str))

            print('A\t{}'.format(' '.join(map(str, alignment))))

if __name__ == '__main__':