This open source software is aimed at quality estimation (QE) for machine translation. It was developed by Lucia Specia's team at the University of Sheffield and includes contributions from a number of researchers between Feb/2011 and Feb/2013. This particular release was made possible through the QuEst project (http://staffwww.dcs.shef.ac.uk/people/L.Specia/projects/quest.html). The code has two main parts: a feature extractor and a machine learning pipeline.
This code implements a number of feature extractors, including most commonly used features in the literature, as well as many of the features used by systems submitted to the WMT2012 shared task on QE. Extractors for new features can be easily added (see the documentation under dist/).
The program itself does not require any installation step. It requires the Java Runtime Environment, and depending on the features to be extracted, a few additional libraries (see below). If you change the code, it can be easily rebuilt using NetBeans, as a NetBeans project is distributed in this release.
The libraries required to compile and run the code are included in the "lib" directory in the root directory of the distribution. The Java libraries should be included there when possible. Here is a list of libraries that should be downloaded and placed in the "lib" directory:
- Stanford POS Tagger
- Berkeley Parser
Apart from these lib files, QuEst requires other external tools / scripts to extract the specified features. The paths for these external tools are set in configuration file under config folder:
- TreeTagger (http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/)
- SRILM (http://www.speech.sri.com/projects/srilm/download.html)
- Tokenizer (available from moses toolkit)
- Truecaser (available from moses toolkit)
Please note that above list is not exhaustive. Advance set of features require external tools, see details in corresponding section.
ant -f build.xml
OR
rebuild using NetBeans (NetBeans project files are distributed in this release)
To prepare:
(1) We provide the system some language resources. These are copied to lang_resources folder. Resources are available from here: http://www.quest.dcs.shef.ac.uk/
(2) You copy these to: lang_resources/[language]/
(3) Edit the configuration file (i.e. config/config_en-es.properties)
We tested our software on Linux and Mac OS. We have not tested it on Windows yet. We provide shell scripts to call the feature extractor for a pre-defined list of features.
For black box features:
./runBB.sh or bash runBB.sh
For glass box features:
./runGB_with_txt.sh or bash runGB.sh
Or
./runGB_with_xml.sh or bash runGB_with_xml.sh
More information about these scripts and the code itself can be found on the development guide (dist/MTFeatures.pdf).
Along with the code, we have provided configuration files and toy resources (SMT training corpus, language models, Giza files, etc) that should make the scripts above run without any problem. The actual resources used for the WMT12 shared task on QE You can download them from: http://www.quest.dcs.shef.ac.uk/
NOTE: One need to adapt the configuration file by providing the paths to the scripts where they are installed on your own system. i.e config/config_en-es.properties
For these features more information about the input resources and how they can be created for new language pairs can be found in specific readme files under the relevant resource folders (all under 'lang_resources') which could be downloaded from: http://www.quest.dcs.shef.ac.uk/
The function of this package of Python scripts is to build models for machine translation (MT) quality estimation (QE). The input files are a set of instances with features that describe sentence pairs (source and target sentences). The features can be extracted using the FeatureExtractor program as explained above.
The program itself does not require any installation step, it is just a matter of running it provided that all the dependencies are installed.
All the machine learning algorithms are implemented by the scikit-learn library. This program provides a command-line interface for some of the implementations contained in this toolkit. In order to be able to run, the program requires that the following packages are installed in your Python distribution:
- numpy ( http://scipy.org/Download )
- scikit-learn ( http://scikit-learn.org/stable/install.html )
- pyyaml ( http://pyyaml.org/ )
Note: Following commands are based on the assumption that all files are under 'learning' directory. The program takes only one input parameter, the configuration file. For example:
python src/learn_model.py config/svr.cfg
The configuration uses the YAML format (http://www.yaml.org/). Its layout is quite straightforward. It is formed by key and value pairs that map directly to dictionaries (in Python) or hash tables with string keys. One example is as follows:
learning:
method: LassoLars
parameters:
alpha: 1.0
max_iter: 500
normalize: True
fit_intercept: True
fit_path: True
verbose: False
Each keyword followed by a ":" represents an entry in a hash. In this example, the dictionary contains an entry "learning" that points to another dictionary with two entries "method" and "parameters". The values of each entry can be lists, dictionaries or primitive values like floats, integers, booleans or strings. Please note that each level in the example above is indented with 4 spaces.
For more information about the YAML format please refer to http://www.yaml.org/ .
The configuration file is composed of three main parts: input and generic options, feature selection, and learning.
Input comprises the following four parameters:
x_train: ./data/features/wmt2012_qe_baseline/training.qe.baseline.tsv
y_train: ./data/features/wmt2012_qe_baseline/training.effort
x_test: ./data/features/wmt2012_qe_baseline/test.qe.baseline.tsv
y_test: ./data/features/wmt2012_qe_baseline/test.effort
The first two are the paths to the files containing the features for the training set and the responses for the training set, respectively. The last two options refer to the test dataset features and response values, respectively.
The format of the feature files is any format that uses a character to separate the columns. The default is the tabulator char (tab, or '\t') as this is the default format generated by the features extractor module.
Two other options are available:
scale: true
separator: "\t"
'scale' applies scikit-learn's scale() function to remove the mean and divide by the unit standard deviation for each feature. This function is applied to the concatenation of the training and test sets. More information about the scale function implemented by scikit-learn can be found at http://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.scale.html
'separator' sets the character used to delimit the columns in the input files.
Configuration files for some of the implemented algorithms are available in the config/ directory.
Currently these are the algorithms available in the script:
- SVR: epsilon Support Vector Regression
The parameters exposed in the "Parameters" section of the configuration file are:
- C
- epsilon
- kernel
- degree
- gamma
- tol
- verbose
Documentation about these parameters is available at http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR
- SVC: C-Support Vector Classification
The parameters exposed in the "Parameters" section of the configuration file are:
- C
- coef0
- kernel
- degree
- gamma
- tol
- verbose
Documentation about these parameters is available at http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC
- LassoCV: Lasso linear model with iterative fitting along a regularization path.
The best model is selected by cross-validation.
The parameters exposed in the "Parameters" section of the configuration file are:
- eps
- n_alphas
- normalize
- precompute
- max_iter
- tol
- cv
- verbose
Documentation about these parameters is available at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html#sklearn.linear_model.LassoCV
- LassoLars: Lasso model fit with Least Angle Regression (Lars)
The parameters exposed in the "Parameters" section of the configuration file are:
- alpha
- fit_intercept
- verbose
- normalize
- max_iter
- fit_path
Documentation about these parameters is available at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLars.html#sklearn.linear_model.LassoLars
- LassoLarsCV: Cross-validated Lasso using the LARS algorithm
The parameters exposed in the "Parameters" section of the configuration file are:
- max_iter
- normalize
- max_n_alphas
- n_jobs
- cv
- verbose
Documentation about these parameters is available at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsCV.html#sklearn.linear_model.LassoLarsCV
It is possible to optimize the parameters of the chosen method. This option is set by the "optimize" setting under "learning" in the configuration file. The script uses the scikit-learn's GridSearchCV implementation of grid search to optimize parameters using cross-validation. To optimize the C, gamma and epsilon parameters for the SVR, the learning section of the configuration file could look as follows:
learning:
method: SVR
optimize:
kernel: [rbf]
C: [1, 10, 2]
gamma: [0.0001, 0.01, 2]
epsilon: [0.1, 0.2, 2]
cv: 3
n_jobs: 1
verbose: True
The parameter kernel is a list of strings representing the available kernels implemented by scikit-learn. In this example only the "RBF" kernel is used.
-
The SVR parameters C, gamma and epsilon are set with lists with 3 indexes:
- the beginning of the range (begin value included)
- the end of the range (end value included)
- the number of samples to be generated within [beginning, end]
-
The remaining parameters modify the behavior of the GridSearchCV class:
- cv is the number of cross-validation folds
- n_jobs is the number of parallel jobs scheduled to run the CV process
- verbose is a boolean or integer value indicating the level of verbosity
For more information about the GridSearchCV class please refer to http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV
Another possible option is to perform feature selection prior to the learning process. To set up a feature selection algorithm it is necessary to add the "feature_selection" section to the configuration file. This section is independent of the "learning" section:
feature_selection:
method: RandomizedLasso
parameters:
cv: 10
learning:
...
Currently, the following feature selection algorithms are available:
-
RandomizedLasso: works by resampling the training data and computing a Lasso on each resampling. The features selected more often are good features. The exposed parameters are:
- alpha
- scaling
- sample_fraction
- n_resampling
- selection_threshold
- fit_intercept
- verbose
- normalize
- max_iter
- n_jobs
These parameters and the method are documented at: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RandomizedLasso.html#sklearn.linear_model.RandomizedLasso
-
ExtraTreesClassifier: meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. The exposed parameters are:
- n_estimators
- max_depth
- min_samples_split
- min_samples_leaf
- min_density
- max_features
- bootstrap
- compute_importances
- n_jobs
- random_state
- verbose
Documentation about the parameters and the method can be found at: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifie
The function of this package of Python scripts is to build models for machine translation (MT) quality estimation (QE) using Gaussian Process. The input files are a set of instances with features that describe sentence pairs (source and target sentences).
The program itself does not require any installation step, it is just a matter of running it provided that all the dependencies are installed.
All the machine learning algorithms are implemented by the GPy library. This program provides a command-line interface for some of the implementations contained in this toolkit. In order to be able to run, the program requires that the following packages are installed in your Python distribution:
- GPy ( https://pypi.python.org/pypi/GPy )
- sciPy ( http://scipy.org/Download )
Note: Following commands are based on the assumption that all files are under 'learning' directory. The program takes only one input parameter, the configuration file. For example:
python src/GP_wmt_regression.py
Please set the path in above script to the input files. e.g X = np.genfromtxt('train-79-features.qe.tsv') test_X = np.genfromtxt('test-79-features.qe.tsv') Y = np.genfromtxt('qe_reference_en-es.train.effort').reshape(-1, 1) test_Y = np.genfromtxt('qe_reference_en-es.test.effort').reshape(-1, 1)
A number of people contributed to this code:
-- Catalina Hallett, who provided an earlier version of the feature extraction code. -- José Guilherme Camargo de Souza, who took care of the machine learning pipeline and helped with the feature extraction code in various ways. -- Kashif Shah, who is the main person taking care of the code nowadays. -- All of the following who visited Sheffield and added their own feature extractors to the code and also contributed with testing it: -- Eleftherios Avramidis -- Christian Buck -- David Langlois -- Erwan Moreau -- Quang Ngoc Luong -- Raphael Rubino
We thank the European commission Pascal2 Network of Excellence for the funding the visits of these and other researchers to Sheffield under the "Harvest" scheme, as well as the European commission FP7 QTLauncePad CSA for funding further development and maintenance of this software.
The license for the Java code and any python and shell scripts developed here is the very permissive BSD License (http://en.wikipedia.org/wiki/BSD_licenses). For pre-existing code and resources, e.g., scikit-learn and Berkeley parser, please check their websites.