translation is a challenging task that traditionally involves large statistical models developed using highly sophisticated linguistic knowledge.

Neural machine translation is the use of deep neural networks for the problem of machine translation.

In this tutorial, you will discover how to a neural machine translation system for translating German phrases to English.

After completing this tutorial, you will know:

  • How to clean and prepare data ready to train a neural machine translation system.
  • How to develop an encoder-decoder model for machine translation.
  • How to use a trained model for inference on new input phrases and evaluate the model skill.

Let’s get started.

How to Develop a Neural Machine Translation System in Keras

How to Develop a Neural Machine Translation System in
Photo by Björn Groß, some rights reserved.

Tutorial Overview

This tutorial is divided into 4 parts; they are:

  1. German to English Translation Dataset
  2. Preparing the Text Data
  3. Train Neural Translation Model
  4. Evaluate Neural Translation Model

Python Environment

This tutorial assumes you have a Python 3 SciPy environment installed.

You must have Keras (2.0 or higher) installed with either the TensorFlow or Theano backend.

The tutorial also assumes you have NumPy and Matplotlib installed.

If you need help with your environment, see this post:


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German to English Translation Dataset

In this tutorial, we will use a dataset of German to English terms used as the basis for flashcards for language learning.

The dataset is available from the ManyThings.org website, with examples drawn from the Tatoeba Project. The dataset is comprised of German phrases and their English counterparts and is intended to be used with the Anki flashcard software.

The page provides a list of many language pairs, and I encourage you to explore other languages:

The dataset we will use in this tutorial is available for download here:

Download the dataset to your current working directory and decompress; for example:




You will have a file called deu.txt that contains 152,820 pairs of English to German phases, one pair per line with a tab separating the language.

For example, the first 5 lines of the file look as follows:



We will frame the prediction problem as given a sequence of words in German as input, translate or predict the sequence of words in English.

The model we will develop will be suitable for some beginner German phrases.

Preparing the Text Data

The next step is to prepare the text data ready for modeling.

Take a look at the raw data and note what you see that we might need to handle in a data cleaning operation.

For example, here are some observations I note from reviewing the raw data:

  • There is punctuation.
  • The text contains uppercase and lowercase.
  • There are special characters in the German.
  • There are duplicate phrases in English with different translations in German.
  • The file is ordered by sentence length with very long sentences toward the end of the file.

Did you note anything else that could be important?
Let me know in the comments below.

A good text cleaning procedure may handle some or all of these observations.

Data preparation is divided into two subsections:

  1. Clean Text
  2. Split Text

1. Clean Text

First, we must load the data in a way that preserves the Unicode German characters. The function below called load_doc() will load the file as a blob of text.



Each line contains a single pair of phrases, first English and then German, separated by a tab character.

We must split the loaded text by line and then by phrase. The function to_pairs() below will split the loaded text.



We are now ready to clean each sentence. The specific cleaning operations we will perform are as follows:

  • Remove all non-printable characters.
  • Remove all punctuation characters.
  • Normalize all Unicode characters to ASCII (e.g. Latin characters).
  • Normalize the case to lowercase.
  • Remove any remaining tokens that are not alphabetic.

We will perform these operations on each phrase for each pair in the loaded dataset.

The clean_pairs() function below implements these operations.



Finally, now that the data has been cleaned, we can save the list of phrase pairs to a file ready for use.

The function save_clean_data() uses the pickle API to save the list of clean text to file.

Pulling all of this together, the complete example is listed below.



Running the example creates a new file in the current working directory with the cleaned text called english-german.pkl.

Some examples of the clean text are printed for us to evaluate at the end of the run to confirm that the clean operations were performed as expected.



2. Split Text

The clean data contains a little over 150,000 phrase pairs and some of the pairs toward the end of the file are very long.

This is a good number of examples for developing a small translation model. The complexity of the model increases with the number of examples, length of phrases, and size of the vocabulary.

Although we have a good dataset for modeling translation, we will simplify the problem slightly to dramatically reduce the size of the model required, and in turn the training time required to fit the model.

You can explore developing a model on the fuller dataset as an extension; I would love to hear how you do.

We will simplify the problem by reducing the dataset to the first 10,000 examples in the file; these will be the shortest phrases in the dataset.

Further, we will then stake the first 9,000 of those as examples for training and the remaining 1,000 examples to test the fit model.

Below is the complete example of loading the clean data, splitting it, and saving the split portions of data to new files.



Running the example creates three new files: the english-german-both.pkl that contains all of the train and test examples that we can use to define the parameters of the problem, such as max phrase lengths and the vocabulary, and the english-german-train.pkl and english-german-test.pkl files for the train and test dataset.

We are now ready to start developing our translation model.

Train Neural Translation Model

In this section, we will develop the translation model.

This involves both loading and preparing the clean text data ready for modeling and defining and training the model on the prepared data.

Let’s start off by loading the datasets so that we can prepare the data. The function below named load_clean_sentences() can be used to load the train, test, and both datasets in turn.



We will use the “both” or combination of the train and test datasets to define the maximum length and vocabulary of the problem.

This is for simplicity. Alternately, we could define these properties from the training dataset alone and truncate examples in the test set that are too long or have words that are out of the vocabulary.

We can use the Keras Tokenize class to map words to integers, as needed for modeling. We will use separate tokenizer for the English sequences and the German sequences. The function below-named create_tokenizer() will train a tokenizer on a list of phrases.



Similarly, the function named max_length() below will find the length of the longest sequence in a list of phrases.



We can call these functions with the combined dataset to prepare tokenizers, vocabulary sizes, and maximum lengths for both the English and German phrases.



We are now ready to prepare the training dataset.

Each input and output sequence must be encoded to integers and padded to the maximum phrase length. This is because we will use a word embedding for the input sequences and one hot encode the output sequences The function below named encode_sequences() will perform these operations and return the result.



The output sequence needs to be one-hot encoded. This is because the model will predict the probability of each word in the vocabulary as output.

The function encode_output() below will one-hot encode English output sequences.



We can make use of these two functions and prepare both the train and test dataset ready for training the model.



We are now ready to define the model.

We will use an encoder-decoder LSTM model on this problem. In this architecture, the input sequence is encoded by a front-end model called the encoder then decoded word by word by a backend model called the decoder.

The function define_model() below defines the model and takes a number of arguments used to configure the model, such as the size of the input and output vocabularies, the maximum length of input and output phrases, and the number of memory units used to configure the model.

The model is trained using the efficient Adam approach to stochastic gradient descent and minimizes the categorical loss function because we have framed the prediction problem as multi-class classification.

The model configuration was not optimized for this problem, meaning that there is plenty of opportunity for you to tune it and lift the skill of the translations. I would love to see what you can come up with.



Finally, we can train the model.

We train the model for 30 epochs and a batch size of 64 examples.

We use checkpointing to ensure that each time the model skill on the test set improves, the model is saved to file.



We can tie all of this together and fit the neural translation model.

The complete working example is listed below.



Running the example first prints a summary of the parameters of the dataset such as vocabulary size and maximum phrase lengths.



Next, a summary of the defined model is printed, allowing us to confirm the model configuration.



A plot of the model is also created providing another perspective on the model configuration.

Plot of Model Graph for NMT

Plot of Model Graph for NMT

Next, the model is trained.

Each epoch takes about 30 seconds on modern CPU hardware; no GPU is required.

During the run, the model will be saved to the file model.h5, ready for inference in the next step.



Evaluate Neural Translation Model

We will evaluate the model on the train and the test dataset.

The model should perform very well on the train dataset and ideally have been generalized to perform well on the test dataset.

Ideally, we would use a separate validation dataset to help with model selection during training instead of the test set. You can try this as an extension.

The clean datasets must be loaded and prepared as before.



Next, the best model saved during training must be loaded.



Evaluation involves two steps: first generating a translated output sequence, and then repeating this process for many input examples and summarizing the skill of the model across multiple cases.

Starting with inference, the model can predict the entire output sequence in a one-shot manner.



This will be a sequence of integers that we can enumerate and lookup in the tokenizer to map back to words.

The function below, named word_for_id(), will perform this reverse mapping.



We can perform this mapping for each integer in the translation and return the result as a string of words.

The function predict_sequence() below performs this operation for a single encoded source phrase.



Next, we can repeat this for each source phrase in a dataset and compare the predicted result to the expected target phrase in English.

We can print some of these comparisons to screen to get an idea of how the model performs in practice.

We will also calculate the BLEU scores to get a quantitative idea of how well the model has performed.

The evaluate_model() function below implements this, calling the above predict_sequence() function for each phrase in a provided dataset.



We can tie all of this together and evaluate the loaded model on both the training and test datasets.

The complete code listing is provided below.



Running the example first prints examples of source text, expected and predicted translations, as well as scores for the training dataset, followed by the test dataset.

Your specific results will differ given the random shuffling of the dataset and the stochastic nature of neural networks.

Looking at the results for the test dataset first, we can see that the translations are readable and mostly correct.

For example: “ich liebe dich” was correctly translated to “i love you“.

We can also see that the translations were not perfect, with “ich konnte nicht gehen” translated to “i cant go” instead of the expected “i couldnt walk“.

We can also see the BLEU-4 score of 0.51, which provides an upper bound on what we might expect from this model.



Looking at the results on the test set, do see readable translations, which is not an easy task.

For example, we see “ich mag dich nicht” correctly translated to “i dont like you“.

We also see some poor translations and a good case that the model could suffer from further tuning, such as “ich bin etwas beschwipst” translated as “i a bit bit” instead of the expected “im a bit tipsy

A BLEU-4 score of 0.076238 was achieved, providing a baseline skill to improve upon with further improvements to the model.



Extensions

This section lists some ideas for extending the tutorial that you may wish to explore.

  • Data Cleaning. Different data cleaning operations could be performed on the data, such as not removing punctuation or normalizing case, or perhaps removing duplicate English phrases.
  • Vocabulary. The vocabulary could be refined, perhaps removing words used less than 5 or 10 times in the dataset and replaced with “unk“.
  • More Data. The dataset used to fit the model could be expanded to 50,000, 100,000 phrases, or more.
  • Input Order. The order of input phrases could be reversed, which has been reported to lift skill, or a Bidirectional input layer could be used.
  • Layers. The encoder and/or the decoder models could be expanded with additional layers and trained for more epochs, providing more representational capacity for the model.
  • Units. The number of memory units in the encoder and decoder could be increased, providing more representational capacity for the model.
  • Regularization. The model could use regularization, such as weight or activation regularization, or the use of dropout on the LSTM layers.
  • Pre-Trained Word Vectors. Pre-trained word vectors could be used in the model.
  • Recursive Model. A recursive formulation of the model could be used where the next word in the output sequence could be conditional on the input sequence and the output sequence generated so far.

Further Reading

This section provides more resources on the topic if you are looking to go deeper.

Summary

In this tutorial, you discovered how to develop a neural machine translation system for translating German phrases to English.

Specifically, you learned:

  • How to clean and prepare data ready to train a neural machine translation system.
  • How to develop an encoder-decoder model for machine translation.
  • How to use a trained model for inference on new input phrases and evaluate the model skill.

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.

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