Paper: Exact Maximum Inference for the Fertility Hidden Markov Model

ACL ID P13-2002
Title Exact Maximum Inference for the Fertility Hidden Markov Model
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

The notion of fertility in word alignment (the number of words emitted by a sin- gle state) is useful but difficult to model. Initial attempts at modeling fertility used heuristic search methods. Recent ap- proaches instead use more principled ap- proximate inference techniques such as Gibbs sampling for parameter estimation. Yet in practice we also need the single best alignment, which is difficult to find us- ing Gibbs. Building on recent advances in dual decomposition, this paper introduces an exact algorithm for finding the sin- gle best alignment with a fertility HMM. Finding the best alignment appears impor- tant, as this model leads to a substantial improvement in alignment quality.