A Novel Graph-based Compact Representation of Word Alignment

In this paper, we propose a novel compact representation called weighted bipartite hypergraph to exploit the fertility model, which plays a critical role in word alignment. However, estimating the probabilities of rules extracted from hypergraphs is an NP-complete problem, which is computationally infeasible. Therefore, we propose a divide-and-conquer strategy by decomposing a hypergraph into a set of independent subhypergraphs. The experiments show that our approach outperforms both 1-best and n-best alignments.