Post-Editing Task Tutorial

This tutorial will guide you through designing your first experiment in Neural Monkey.

Before we get started with the tutorial, please check that you have the Neural Monkey package properly installed and working.

Part I. - The Task

This section gives an overall description of the task we will try to solve in this tutorial. To make things more interesting than plain machine translation, let’s try automatic post-editing task (APE, rhyming well with Neural Monkey).

In short, automatic post-editing is a task, in which we have a source language sentence (let’s call it f, as grown-ups do), a machine-translated sentence of f (I actually don’t know what grown-ups call this, so let’s call this e'), and we are expected to generate another sentence in the same language as e' but cleaned of all the errors that the machine translation system have made (let’s call this cleaned sentence e). Consider this small example:

Source sentence f:
Bärbel hat eine Katze.
Machine-translated sentence e':
Bärbel has a dog.
Corrected translation e:
Bärbel has a cat.

In the example, the machine translation system wrongly translated the German word “Katze” as the English word “dog”. It is up to the post-editing system to fix this error.

In theory (and in practice), we regard the machine translation task as searching for a target sentence e* that has the highest probability of being the translation given the source sentence f. You can put it to a formula:

e* = argmax_e p(e|f)

In the post-editing task, the formula is slightly different:

e* = argmax_e p(e|f, e')

If you think about this a little, there are two ways one can look at this task. One is that we are translating the machine-translated sentence from a kind of synthetic language into a proper one, with additional knowledge what the source sentence was. The second view regards this as an ordinary machine translation task, with a little help from another MT system.

In our tutorial, we will assume the MT system used to produce the sentence e' was good enough. We thus generally trust it and expect only to make small edits to the translated sentence in order to make it fully correct. This means that we don’t need to train a whole new MT system that would translate the source sentences from scratch. Instead, we will build a system that will tell us how to edit the machine translated sentence e'.

Part II. - The Edit Operations

How can an automatic system tell us how to edit a sentence? Here’s one way to do it: We will design a set of edit operations and train the system to generate a sequence of these operations. If we consider a sequence of edit operations a function R (as in rewrite), which transforms one sequence to another, we can adapt the formulas above to suit our needs more:

R* = argmax_R p(R(e')|f, e')
e* = R*(e')

So we are searching for the best edit function R* that, once applied to e', will give us the corrected output e*. Another question is what the class of all possible edit functions should look like, for now we simply limit them to functions that can be defined as sequences of edit operations.

The edit function R processes the input sequence token-by-token in left-to-right direction. It has a pointer to the input sequence, which starts by pointing to the first word of the sequence.

We design three types of edit operations as follows:

  1. KEEP - this operation copies the current word to the output and moves the pointer to the next token of the input,
  2. DELETE - this operation does not emit anything to the output and moves the pointer to the next token of the input,
  3. INSERT - this operation puts a word on the output, leaving the pointer to the input intact.

The edit function applies all its operations to the input sentence. We handle malformed edit sequences simply: if the pointer reaches the end of the input seqence, operations KEEP and DELETE do nothing. If the sequence of edits ends before the end of the input sentence is reached, we apply as many additional KEEP operations as needed to reach the end of the input sequence.

Let’s see another example:

Bärbel  has   a     dog          .
KEEP    KEEP  KEEP  DELETE  cat  KEEP

The word “cat” on the second line is an INSERT operation parameterized by the word “cat”. If we apply all the edit operations to the input (i.e. keep the words “Bärbel”, “has”, “a”, and “.”, delete the word “dog” and put the word “cat” in its place), we get the corrected target sentence.

Part III. - The Data

We are going to use the data for WMT 16 shared APE task. You can get them at the WMT 16 website or directly at the Lindat repository. There are three files in the repository:

  1. TrainDev.zip - contains training and development data set
  2. Test.zip - contains source and translated test data
  3. test_pe.zip - contains the post-edited test data

Now - before we start, let’s create our experiment directory, in which we will place all our work. We shall call it for example exp-nm-ape (feel free to choose another weird string).

Extract all the files into the exp-nm-ape/data directory. Rename the files and directories so you get this directory structure:

exp-nm-ape
|
\== data
    |
    |== train
    |   |
    |   |== train.src
    |   |== train.mt
    |   \== train.pe
    |
    |== dev
    |   |
    |   |== dev.src
    |   |== dev.mt
    |   \== dev.pe
    |
    \== test
        |
        |== test.src
        |== test.mt
        \== test.pe

The data is already tokenized so we don’t need to run any preprocessing tools. The format of the data is plain text with one sentence per line. There are 12k training triplets of sentences, 1k development triplets and 2k of evaluation triplets.

Preprocessing of the Data

The next phase is to prepare the post editing sequences that we should learn during training. We apply the Levenshtein algorithm to find the shortest edit path from the translated sentence to the post-edited sentence. As a little coding excercise, you can implement your own script that does the job, or you may use our preprocessing script from the Neural Monkey package. For this, in the neuralmonkey root directory, run:

scripts/postedit_prepare_data.py \
  --translated-sentences=exp-nm-ape/data/train/train.mt \
  --target-sentences=exp-nm-ape/data/train/train.pe \
      > exp-nm-ape/data/train/train.edits

And the same for the development data.

NOTE: You may have to change the path to the exp-nm-ape directory if it is not located inside the repository root directory.

NOTE 2: There is a hidden option of the preparation script (--target-german=True) which turns on some steps tailored for better processing of German text. In this tutorial, we are not going to use it.

If you look at the preprocessed files, you will see that the KEEP and DELETE operations are represented with special tokens while the INSERT operations are represented simply with the word they insert.

Congratulations! Now, you should have train.edits, dev.edits and test.edits files all in their respective data directories. We can now move to work with Neural Monkey configurations!

Part IV. - The Model Configuration

In Neural Monkey, all information about a model and its training is stored in configuration files. The syntax of these files is a plain INI syntax (more specifically, the one which gets processed by Python’s ConfigParser). The configuration file is structured into a set of sections, each describing a part of the training. In this section, we will go through all of them and write our configuration file needed for the training of the post-editing task.

First of all, create a file called post-edit.ini and put it inside the exp-nm-ape directory. Put all the snippets that we will describe in the following paragraphs into the file.

1 - Datasets

For training, we prepare two datasets. The first dataset will serve for the training, the second one for validation. In Neural Monkey, each dataset contains a number of so called data series. In our case, we will call the data series source, translated, and edits. Each of those series will contain the respective set of sentences.

It is assumed that all series within a given dataset have the same number of elements (i.e. sentences in our case).

The configuration of the datasets looks like this:

[train_dataset]
class=dataset.load_dataset_from_files
s_source="exp-nm-ape/data/train/train.src"
s_translated="exp-nm-ape/data/train/train.mt"
s_edits="exp-nm-ape/data/train/train.edits"

[val_dataset]
class=dataset.load_dataset_from_files
s_source="exp-nm-ape/data/dev/dev.src"
s_translated="exp-nm-ape/data/dev/dev.mt"
s_edits="exp-nm-ape/data/dev/dev.edits"

Note that series names (source, translated, and edits) are arbitrary and defined by their first mention. The s_ prefix stands for “series” and is used only here in the dataset sections, not later when the series are referred to.

These two INI sections represent two calls to function neuralmonkey.config.dataset_from_files, with the series file paths as keyword arguments. The function serves as a constructor and builds an object for every call. So at the end, we will have two objects representing the two datasets.

2 - Vocabularies

Each encoder and decoder which deals with language data operates with some kind of vocabulary. In our case, the vocabulary is just a list of all unique words in the training data. Note that apart the special <keep> and <delete> tokens, the vocabularies for the translated and edits series are from the same language. We can save some memory and perhaps improve quality of the target language embeddings by share vocabularies for these datasets. Therefore, we need to create only two vocabulary objects:

[source_vocabulary]
class=vocabulary.from_dataset
datasets=[<train_dataset>]
series_ids=["source"]
max_size=50000

[target_vocabulary]
class=vocabulary.from_dataset
datasets=[<train_dataset>]
series_ids=["edits", "translated"]
max_size=50000

The first vocabulary object (called source_vocabulary) represents the (English) vocabulary used for this task. The 50,000 is the maximum size of the vocabulary. If the actual vocabulary of the data was bigger, the rare words would be replaced by the <unk> token (hardcoded in Neural Monkey, not part of the 50,000 items), which stands for unknown words. In our case, however, the vocabularies of the datasets are much smaller so we won’t lose any words.

Both vocabularies are created out of the training dataset, as specified by the line datasets=[<train_dataset>] (more datasets could be given in the list). This means that if there are any unseen words in the development or test data, our model will treat them as unknown words.

We know that the languages in the translated series and edits are the same (except for the KEEP and DELETE tokens in the edits), so we create a unified vocabulary for them. This is achieved by specifying series_ids=[edits, translated]. The one-hot encodings (or more precisely, indices to the vocabulary) will be identical for words in translated and edits.

3 - Encoders

Our network will have two inputs. Therefore, we must design two separate encoders. The first encoder will process source sentences, and the second will process translated sentences, i.e. the candidate translations that we are expected to post-edit. This is the configuration of the encoder for the source sentences:

[src_encoder]
class=encoders.recurrent.SentenceEncoder
rnn_size=300
max_input_len=50
embedding_size=300
dropout_keep_prob=0.8
attention_type=decoding_function.Attention
data_id="source"
name="src_encoder"
vocabulary=<source_vocabulary>

This configuration initializes a new instance of sentence encoder with the hidden state size set to 300 and the maximum input length set to 50. (Longer sentences are trimmed.) The sentence encoder looks up the words in a word embedding matrix. The size of the embedding vector used for each word from the source vocabulary is set to 300. The source data series is fed to this encoder. 20% of the weights is dropped out during training from the word embeddings and from the attention vectors computed over the hidden states of this encoder. Note the name attribute must be set in each encoder and decoder in order to prevent collisions of the names of Tensorflow graph nodes.

The configuration of the second encoder follows:

[trans_encoder]
class=encoders.recurrent.SentenceEncoder
rnn_size=300
max_input_len=50
embedding_size=300
dropout_keep_prob=0.8
attention_type=decoding_function.Attention
data_id="translated"
name="trans_encoder"
vocabulary=<target_vocabulary>

This config creates a second encoder for the translated data series. The setting is the same as for the first encoder, except for the different vocabulary and name.

4 - Decoder

Now, we configure perhaps the most important object of the training - the decoder. Without further ado, here it goes:

[decoder]
class=decoders.decoder.Decoder
name="decoder"
encoders=[<trans_encoder>, <src_encoder>]
rnn_size=300
max_output_len=50
embeddings_encoder=<trans_encoder>
dropout_keep_prob=0.8
use_attention=True
data_id="edits"
vocabulary=<target_vocabulary>

As in the case of encoders, the decoder needs its RNN and embedding size settings, maximum output length, dropout parameter, and vocabulary settings.

The outputs of the individual encoders are by default simply concatenated and projected to the decoder hidden state (of rnn_size). Internally, the code is ready to support arbitrary mappings by adding one more parameter here: encoder_projection.

Note that you may set rnn_size to None. Neural Monkey will then directly use the concatenation of encoder states without any mapping. This is particularly useful when you have just one encoder as in MT.

The line embeddings_encoder=<trans_encoder> means that the embeddings (including embedding size) are shared with trans_encoder.

The loss of the decoder is computed against the edits data series of whatever dataset the decoder will be applied to.

5 - Runner and Trainer

As their names suggest, runners and trainers are used for running and training models. The trainer object provides the optimization operation to the graph. In the case of the cross entropy trainer (used in our tutorial), the default optimizer is Adam and it is run against the decoder’s loss, with added L2 regularization (controlled by the l2_weight parameter of the trainer). The runner is used to process a dataset by the model and return the decoded sentences, and (if possible) decoder losses.

We define these two objects like this:

[trainer]
class=trainers.cross_entropy_trainer.CrossEntropyTrainer
decoders=[<decoder>]
l2_weight=1.0e-8

[runner]
class=runners.runner.GreedyRunner
decoder=<decoder>
output_series="greedy_edits"

Note that a runner can only have one decoder, but during training you can train several decoders, all contributing to the loss function.

The purpose of the trainer is to optimize the model, so we are not interested in the actual outputs it produces, only the loss compared to the reference outputs (and the loss is calculated by the given decoder).

The purpose of the runner is to get the actual outputs and for further use, they are collected to a new series called greedy_edits (see the line output_series=) of whatever dataset the runner will be applied to.

6 - Evaluation Metrics

During validation, the whole validation dataset gets processed by the models and the decoded sentences are evaluated against a reference to provide the user with the state of the training. For this, we need to specify evaluator objects which will be used to score the outputted sentences. In our case, we will use BLEU and TER:

[bleu]
class=evaluators.bleu.BLEUEvaluator
name="BLEU-4"

7 - TensorFlow Manager

In order to handle global variables such as how many CPU cores TensorFlow should use, you need to specify a “TensorFlow manager”:

[tf_manager]
class=tf_manager.TensorFlowManager
num_threads=4
num_sessions=1
minimize_metric=True
save_n_best=3

8 - Main Configuration Section

Almost there! The last part of the configuration puts all the pieces together. It is called main and specifies the rest of the training parameters:

[main]
name="post editing"
output="exp-nm-ape/training"
runners=[<runner>]
tf_manager=<tf_manager>
trainer=<trainer>
train_dataset=<train_dataset>
val_dataset=<val_dataset>
evaluation=[("greedy_edits", "edits", <bleu>), ("greedy_edits", "edits", evaluators.ter.TER)]
batch_size=128
runners_batch_size=256
epochs=100
validation_period=1000
logging_period=20

The output parameter specifies the directory, in which all the files generated by the training (used for replicability of the experiment, logging, and saving best models variables) are stored. It is also worth noting, that if the output directory exists, the training is not run, unless the line overwrite_output_dir=True is also included here.

The runners, tf_manager, trainer, train_dataset and val_dataset options are self-explanatory.

The parameter evaluation takes list of tuples, where each tuple contains: - the name of output series (as produced by some runner), greedy_edits here, - the name of the reference series of the dataset, edits here, - the reference to the evaluation algorithm, <bleu> and evaluators.ter.TER in the two tuples here.

The batch_size parameter controls how many sentences will be in one training mini-batch. When the model does not fit into GPU memory, it might be a good idea to start reducing this number before anything else. The larger the batch size, however, the sooner the training should converge to the optimum.

Runners are less memory-demanding, so runners_batch_size can be set higher than batch_size.

The epochs parameter specifies the number of passes through the training data that the training loop should do. There is no early stopping mechanism in Neural Monkey yet, the training can be resumed after the end, however. The training can be safely ctrl+C’ed in any time: Neural Monkey preserves the last save_n_best best model variables saved on the disk.

The validation and logging periods specify how often to measure the model’s performance on the training batch (logging_period) or on validation data (validation_period). Note that both logging and validation involve running the runners over the current batch or the validation data, resp. If this happens too often, the time needed to train the model can significantly grow.

At each validation (and logging), the output is scored using the specified evaluation metrics. The last of the evaluation metrics (TER in our case) is used to keep track of the model performance over time. Whenever the score on validation data is better than any of the save_n_best (3 in our case) previously saved models, the model is saved, discaring unneccessary lower scoring models.

Part V. - Running an Experiment

Now that we have prepared the data and the experiment INI file, we can run the training. If your Neural Monkey installation is OK, you can just run this command from the root directory of the Neural Monkey repository:

bin/neuralmonkey-train exp-nm-ape/post-edit.ini

You should see the training program reporting the parsing of the configuration file, initializing the model, and eventually the training process. If everything goes well, the training should run for 100 epochs. You should see a new line with the status of the model’s performance on the current batch every few seconds, and there should be a validation report printed every few minutes.

As given in the main.output config line, the Neural Monkey creates the directory experiments/training with these files:

  • git_commit - the Git hash of the current Neural Monkey revision.
  • git_diff - the diff between the clean checkout and the working copy.
  • experiment.ini - the INI file used for running the training (a simple copy of the file NM was started with).
  • experiment.log - the output log of the training script.
  • checkpoint - file created by Tensorflow, keeps track of saved variables.
  • events.out.tfevents.<TIME>.<HOST> - file created by Tensorflow, keeps the summaries for TensorBoard visualisation
  • variables.data[.<N>] - a set of files with N best saved models.
  • variables.data.best - a symbolic link that points to the variable file with the best model.

Part VI. - Evaluation of the Trained Model

If you have reached this point, you have nearly everything this tutorial offers. The last step of this tutorial is to take the trained model and to apply it to a previously unseen dataset. For this you will need two additional configuration files. But fear not - it’s not going to be that difficult. The first configuration file is the specification of the model. We have this from Part III and a small optional change is needed. The second configuration file tells the run script which datasets to process.

The optional change of the model INI file prevents the training dataset from loading. This is a flaw in the present design and it is planned to change. The procedure is simple:

  1. Copy the file post-edit.ini into e.g. post-edit.test.ini
  2. Open the post-edit.test.ini file and remove the train_dataset and val_dataset sections, as well as the train_dataset and val_dataset configuration from the [main] section.

Now we have to make another file specifying the testing dataset configuration. We will call this file post-edit_run.ini:

[main]
test_datasets=[<eval_data>]

[eval_data]
class=dataset.load_dataset_from_files
s_source="exp-nm-ape/data/test/test.src"
s_translated="exp-nm-ape/data/test/test.mt"
s_greedy_edits_out="exp-nm-ape/test_output.edits"

The dataset specifies the two input series s_source and s_translated (the candidate MT output output to be post-edited) as in the training. The series s_edits (containing reference edits) is not present in the evaluation dataset, because we do not want to use the reference edits to compute loss at this point. Usually, we don’t even know the correct output at runtime.

Instead, we introduce the output series s_greedy_edits_out (the prefix s_ and the suffix _out are hardcoded in Neural Monkey and the series name in between has to match the name of the series produced by the runner).

The line s_greedy_edits_out= specifies the file where the output should be saved. (You may want to alter the path to the exp-nm-ape directory if it is not located inside the Neural Monkey package root dir.)

We have all that we need to run the trained model on the evaluation dataset. From the root directory of the Neural Monkey repository, run:

bin/neuralmonkey-run exp-nm-ape/post-edit.test.ini exp-nm-ape/post-edit_run.ini

At the end, you should see a new file exp-nm-ape/test_output.edits. As you notice, the contents of this file are the sequences of edit operations, which if applied to the machine translated sentences, generate the output that we want. The final step is to call the provided post-processing script. Again, feel free to write your own as a simple exercise:

scripts/postedit_reconstruct_data.py \
  --edits=exp-nm-ape/test_output.edits \
  --translated-sentences=exp-nm-ape/data/test/test.mt \
    > test_output.pe

Now, you can run the official tools (like mteval or the tercom software available on the WMT 16 website) to measure the score of test_output.pe on the data/test/test.pe reference evaluation dataset.

Part VII. - Conclusions

This tutorial gave you the basic overview of how to design your experiments using Neural Monkey. The sample experiment was the task of automatic post-editing. We got the data from the WMT 16 APE shared task and pre-processed them to fit our needs. We have written the configuration file and run the training. At the end, we evaluated the model on the test dataset.

If you want to learn more, the next step is perhaps to browse the examples directory in Neural Monkey repository and see some further possible setups. If you are planning to just design an experiment using existing modules, you can start by editing one of those examples as well.

If you want to dig in the code, you can browse the repository. Please feel free to fork the repository and to send us pull requests. The API documentation is currently under construction, but it already contains a little information about Neural Monkey objects and their configuraiton options.

Have fun!