Paper: Probabilistic Word Alignment under the $L_0$-norm

ACL ID W11-0320
Title Probabilistic Word Alignment under the $L_0$-norm
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2011
Authors

This paper makes two contributions to the area of single-word based word alignment for bilingual sentence pairs. Firstly, it integrates the – seemingly rather different – works of (Bodrumlu et al., 2009) and the standard prob- abilistic ones into a single framework. Secondly, we present two algorithms to opti- mize the arising task. The first is an iterative scheme similar to Viterbi training, able to han- dle large tasks. The second is based on the in- exact solution of an integer program. While it can handle only small corpora, it allows more insight into the quality of the model and the performance of the iterative scheme. Finally, we present an alternative way to handle prior dictionary knowledge and dis- cuss connections to computing IBM-3 Viterbi alignments.