Smaller Alignment Models for Better Translations: Unsupervised Word Alignment with the l0-norm
Ashish Vaswani, Liang Huang, David Chiang · 2012
Two decades after their invention, the IBM word-based translation models, widely avail-able in the GIZA++ toolkit, remain the dom-inant approach to word alignment and an in-tegral part of many statistical translation sys-tems. Although many models have surpassed them in accuracy, none have supplanted them in practice. In this paper, we propose a simple extension to the IBM models: an `0 prior to en-courage sparsity in the word-to-word transla-tion model. We explain how to implement this extension efficiently for large-scale data (also released as a modification to GIZA++) and demonstrate, in experiments on Czech, Ara-bic, Chinese, and Urdu to English translation, significant improvements over IBM Model 4 in both word alignment (up to +6.7 F1) and translation quality (up to +1.4 Bleu). 1