Guiding Statistical Word Alignment Models With Prior Knowledge
Yonggang Deng, Yuqing Gao · 2007
We present a general framework to incor-porate prior knowledge such as heuristics or linguistic features in statistical generative word alignment models. Prior knowledge plays a role of probabilistic soft constraints between bilingual word pairs that shall be used to guide word alignment model train-ing. We investigate knowledge that can be derived automatically from entropy princi-ple and bilingual latent semantic analysis and show how they can be applied to im-prove translation performance. 1