Example-based rescoring of statistical machine translation output
Michael D. Paul, Eiichiro Sumita, Seiichi Yamamoto · 2004
Conventional statistical machine translation (SMT) approaches might not be able to find a good translation due to problems in its statistical models (due to data sparseness during the estimation of the model parameters) as well as search errors during the decoding process.This paper 1 presents an example-based rescoring method that validates SMT translation candidates and judges whether the selected decoder output is good or not.Given such a validation filter, defective translations can be rejected.The experiments show a drastic improvement in the overall system performance compared to translation selection methods based on statistical scores only.