donglixp/wmt-trueskill

Data and code used in the 2014 WMT, "Efficient Elicitation of Annotations for Human Evaluation of Machine Translation"

TrueSkill for WMT

Source code used in 2014 WMT paper, "Efficient Elicitation of Annotations for Human Evaluation of Machine Translation"

• Keisuke Sakaguchi (keisuke[at]cs.jhu.edu)
• Matt Post
• Benjamin Van Durme

Last updated: June 12th, 2015

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This document describes the proposed method described in the following paper:

@InProceedings{sakaguchi-post-vandurme:2014:W14-33,
  author    = {Sakaguchi, Keisuke  and  Post, Matt  and  Van Durme, Benjamin},
  title     = {Efficient Elicitation of Annotations for Human Evaluation of Machine Translation},
  booktitle = {Proceedings of the Ninth Workshop on Statistical Machine Translation},
  month     = {June},
  year      = {2014},
  address   = {Baltimore, Maryland, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {1--11},
  url       = {http://www.aclweb.org/anthology/W14-3301}
}

Prerequisites (and optional) python modules:

• trueskill (http://trueskill.org/), for running TrueSkill model
• (optional) matplotlib (http://matplotlib.org/), for visualizing result
• (optional) sympy (http://sympy.org/), for tuning

Example Procedure:

• 0. Preprocessing: converting an xml file (from Appraise) to a csv file.
  • mkdir result if not exist.
  • cd data
  • python xml2csv.py ABC.xml
  • The xml/csv file must consist of a single language pair.
• 1. Training: run python infer_TS.py (TrueSkill) in the src directory.
  • cd ../src
  • cat ../data/ABC.csv |python infer_TS.py ../result/ABC -n 2 -d 0 -s 2
  • for more details: python infer_TS.py --help
  • You can change other parameters in infer_TS.py, if needed.
  • For clustering (i.e. grouped ranking), we need to execute multiple runs of infer_TS.py (100+ is recommended) for each language pair (e.g. fr-en from fr-en0 to fr-en99).
  • You will get the result named OUT_ID_mu_sigma.json in the result directory
  • For using Expected Win, run python infer_EW.py -s 2 ../result/ABC
• 2. To see the grouped ranking, run cluster.py in the eval directory.
  • cd ../eval
  • python cluster.py fr-en ../result/fr-en -n 100 -by-rank -pdf
  • for more details: python cluster.py --help
  • pdf option might cause RuntimeError, but please check if a pdf file is successfully generated.
• 3. (optional) To tune decision radius in (accuracy), run tune_acc.py.
  • e.g. cat data/sample-fr-en-{dev|test}.csv |python src/eval_acc.py -d 0.1 -i result/fr-en0_mu_sigma.json
• 4. (optional) To see the next systems to be compared, run python src/scripts/next_comparisons.py *_mu_sigma.json N
  • This outputs the next comparison under the current result mu and sigma (.json) for N free-for-all matches.

Questions and comments:

• Please e-mail to Keisuke Sakaguchi (keisuke[at]cs.jhu.edu).