@inproceedings{pado09:_textual_entail_featur_for_machin_trans_evaluat,
  abstract = {We present two regression models for the prediction of pairwise preference judgments among MT hypotheses. Both models are based on feature sets that are motivated by textual entailment and incorporate lexical similarity as well as local syntactic features and specific semantic phenomena. One model predicts absolute scores; the other one direct pairwise judgments. We find that both models are competitive with regression models built over the scores of established MT evaluation metrics. Further data analysis clarifies the complementary behavior of the two feature sets.},
  added-at = {2017-04-03T19:29:52.000+0200},
  address = {Athens, Greece},
  author = {Pad\'o, Sebastian and Galley, Michel and Manning, Christopher D. and Jurafsky, Daniel},
  biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c9b132a658213688040b224a6f62b1fe/sp},
  booktitle = {Proceedings of the EACL Workshop on Machine Translation},
  interhash = {a9e4288f24b6c1d86077ed4eafdaff64},
  intrahash = {c9b132a658213688040b224a6f62b1fe},
  keywords = {myown workshop},
  pages = {37--41},
  timestamp = {2017-04-03T17:30:49.000+0200},
  title = {Textual Entailment Features for Machine Translation Evaluation},
  url = {http://www.aclweb.org/anthology/W09-0404.pdf},
  year = 2009
}