Probabilistic Word Alignment under the $L_0$-norm

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 probabilistic ones into a single framework. Secondly, we present two algorithms to optimize the arising task. The first is an iterative scheme similar to Viterbi training, able to handle large tasks. The second is based on the inexact 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 discuss connections to computing IBM-3 Viterbi alignments.