/confidence_intervals

Bootstrap resampling for some tasks

Primary LanguageShell

confidence_intervals

Bootstrap resampling for some tasks

MT quality: Bleu - TER - Meteor

Usage:	 confidence intervals.sh <-r reference> <-t hypothesis> <-n nreps>  <-l lan> 
 	                          [-b baseline] [-i interval] [-y] [-v] [-h] 
 	 This script will take up a reference file and a hypothesis file and compute TER and BLEU confidence 
 	 intervals by means of bootstrapping. Note: This script needs a *modified* version of TERCOM and 
 	 multi-bleu.perl. These two modified versions are included into this script for simplicity purposes and 
 	 unpacked on the fly. If [-b baseline] is specified, pairwise improvement intervals will also be computed. 
 Input:	 -r reference: file containing the reference translations. 
 	 -b baseline: file containing the baseline translations. If specified, pair 
 	 -t hypothesis: file containing the (machine) translations to be evaluated. 
 	 -n nreps: number of repetitions to do via bootstrapping. 
 	 -i interval: confidence interval to compute (default 95) 
 	 -y: do not delete temporary files.
   -l: language (required for Meteor).  
 	 -v: activate verbose mode (set -x). 
 	 -h: show this help and exit. 
 Output: - confidence interval

IMT effort estimation: WSR - MAR (or other metrics)

Usage:	 imt_confindence intervals.sh <-t scores> <-n nreps> 
 	                          [-b baseline] [-i interval] [-y] [-v] [-h] 
 	 This script will take up a WSR and MAR scores file and compute MAR and WSR confidence 
 	 intervals by means of bootstrapping. If [-b baseline] is specified, pairwise improvement 
 	 intervals will also be computed. 
 Input:	 -b baseline: file containing the baseline scores. If specified, pair 
 	 -t scores: file containing the scores to be evaluated. 
 	 -n nreps: number of repetitions to do via bootstrapping. 
 	 -i interval: confidence interval to compute (default 95) 
 	 -y: do not delete temporary files. 
 	 -v: activate verbose mode
 	 -h: show this help and exit. 
 Output: - confidence interval

Approximate Randomization Testing

Requires art: https://github.com/smartschat/art .

Two-sided paired approximate randomization tests to assess the statistical significance of the difference in performance between two systems, A and B.

art.sh <-r reference> <-t output-systemB > <-n nreps> <-l lan>