11-928 Master’s Thesis Symmetric Probabilistic Alignment

The CMU Example-Based Machine Translation (EBMT) system has been deployed successfully in many projects for years. But even though a good alignment algorithm is essential since the CMU EBMT system uses parallel corpora, it has relatively less studied than other components of EBMT. For this reason, we developed a new alignment algorithm which uses statistical information drawn from parallel corpora and heuristics based on human linguistic knowledge. Unlike most alignment approaches in Statistical Machine Translation (SMT) systems, our alignment algorithm uses only bilingual dictionaries as statistical information trained from other systems, calculates alignment scores bi-directionally and aims at aligning up to 8 words long source fragments. In our experiments so far, it outperformed the old heuristic-based alignment algorithm in both alignment accuracy and translation accuracy in EBMT. Its performance was very close to the the state-of-the-art in SMT systems for which we picked IBM Model 4 for comparison, and a combination of our new method and IBM Model 4 performed best.