Learns statistical models of human translation processes (HTPs) from keylogging and eye tracking data from recorded translation sessions, and uses these models to segment recorded translation sessions into HTP phases.

The modelling approach is described in detail in

Läubli, Samuel. 2014. Statistical Modelling of Human Translation Processes. MSc Thesis, School of Informatics, The University of Edinburgh.

@mastersthesis{laubli2014msc,
	Author = {L\"{a}ubli, Samuel},
	Title = {Statistical Modelling of Human Translation Processes},
	School = {School of Informatics, The University of Edinburgh},
	Address = {Edinburgh, UK},
	Year = {2014}}

segcats depends on the following frameworks and libraries, which you'll need to install before running any of the modules described below:

• Python 2.7.x
• NumPy > 1.8 (see http://www.numpy.org/)
• lxml (see http://lxml.de)
• Scikit-learn version 0.14 (see http://scikit-learn.org/0.14/)

After downloading your copy of segcats, you'll also need to add it to your PYTHONPATH:

export PYTHONPATH=$PYTHONPATH:/your/path/to/the/base_directory/of/segcats

The feature extraction process is described in detail in Section 4.1 of the thesis referenced above. To convert recorded CasMaCat XML Logs (CFT14 format) into sequences of feature vectors, use extract.py. Example:

python extract.py -w 500 -3 -k -o sample_data/training_extracted/ sample_data/training/*.xml

parametrises all recorded translation sessions in sample_data/training_extracted/ into time windows of length -w=500ms, using adaptor 3 (-3), i.e., counts of keystrokes, mouse clicks, and eye tracking data. Run python extract.py --help for more information.

The model learning and parameter optimisation procedure is described in detail in Section 4.2 of the thesis referenced above. To train a model on the parametrised translation sessions obtained through the above example, run

python train.py -k 6 -m 10 -o model.xml training_extracted/*.csv

This trains a GMM-HMM model with -k=6 hidden states (HTPs) and -m=10 Gaussian mixture components per hidden state. Run python train.py --help for more information.

To add the most likely hidden state to each observation of a parametrised translation session (see above), run

python decode.py -m model.xml training_extracted/P07_P1.xml.csv P07_P1.tagged.csv

where training_extracted/P07_P1.xml.csv is the session to be tagged, and P07_P1.tagged.csv is the file that the tagged version of this session should be written to.

The output of decode.py can be visualised using viscats.

For further information, please have a look at the class documentation of the individual modules, and feel free to contact me at slaubli ät inf dot ed dot ac dot uk for questions and/or feedback.