Segment-based Interactive Machine Translation implementation based on Moses XML scheme.
Moses and mgiza are needed in order to run this software. Alternatively, you can run it through Docker.
This software requires of an alignment model. Therefore, prior to its use, alignments must be trained by doing:
tools/alignments.sh -s src_file -t tgt_file -o output_file -m mgiza_bin {options}
options:
-b: use IBM Model 1. (Default: Use HMM.)
where src_file
and tgt_file
are the source and target of the training dataset; output_file
is the file in which to store the alignments (which will be required for using the segment-based software); mgiza_bin
is the path to mgiza's bin folder (e.g., /opt/moses/mgiza/mgizapp/bin); and the -b
flags switches from using Hidden Markov Models to using IBM Model 1.
You can simulate a user working on a segment-based IMT framework by doing:
simulation.py [-h] -s source_file -r reference_file -c moses_ini -a
alignments_file [-v] [-x] [-p threshold] [-m moses_bin]
-l log_file
optional arguments:
-h, --help show this help message and exit
-s source_file, --sources source_file
file containing the source segments.
-r reference_file, --references reference_file
file containing the reference segments.
-c moses_ini, --config moses_ini
file containing moses configuration.
-a alignments_file, --alignments alignments_file
file containing the alignments generated by alignments.sh
-v, --verbose Activate verbose mode.
-x, --xml Show XML markup.
-p threshold, --probability threshold
probability threshold. (Default 0.)
-m moses_bin, --moses moses_bin
Path to moses bin. (Default: /opt/moses/bin/moses.)
-l log_file, --log log_file
File to store the log.
If you want to run this software through Docker you can have a look at this repo.
On using this software, please cite the following paper:
Miguel Domingo and Álvaro Peris and Francisco Casacuberta. Segment-Based Interactive-Predictive Machine Translation. Machine Translation Journal, 31:163–185, 2017