TESLA: Translation Evaluation of Sentences with Linear-Programming-Based Analysis
Chang Liu, Daniel Dahlmeier, Hwee Tou Ng · 2010
We present TESLA-M and TESLA, two novel automatic machine translation eval-uation metrics with state-of-the-art perfor-mances. TESLA-M builds on the suc-cess of METEOR and MaxSim, but em-ploys a more expressive linear program-ming framework. TESLA further exploits parallel texts to build a shallow seman-tic representation. We evaluate both on the WMT 2009 shared evaluation task and show that they outperform all participating systems in most tasks. 1