Paper: Unsupervised Word Alignment with Arbitrary Features

ACL ID P11-1042
Title Unsupervised Word Alignment with Arbitrary Features
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011

We introduce a discriminatively trained, glob- ally normalized, log-linear variant of the lex- ical translation models proposed by Brown et al. (1993). In our model, arbitrary, non- independent features may be freely incorpo- rated, thereby overcoming the inherent limita- tion of generative models, which require that features be sensitive to the conditional inde- pendencies of the generative process. How- ever, unlike previous work on discriminative modeling of word alignment (which also per- mits the use of arbitrary features), the param- eters in our models are learned from unanno- tated parallel sentences, rather than from su- pervised word alignments. Using a variety of intrinsic and extrinsic measures, including translation performance, we show our model yields better alignments than ...