Paper: Annealing Structural Bias In Multilingual Weighted Grammar Induction

ACL ID P06-1072
Title Annealing Structural Bias In Multilingual Weighted Grammar Induction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

We first show how a structural locality bias can improve the accuracy of state-of-the-art dependency grammar induction models trained by EM from unannotated examples (Klein and Manning, 2004). Next, by annealing the free parame- ter that controls this bias, we achieve further improvements. We then describe an alternative kind of structural bias, to- ward “broken” hypotheses consisting of partial structures over segmented sentences, and show a similar pattern of im- provement. We relate this approach to contrastive estimation (Smith and Eisner, 2005a), apply the latter to grammar in- duction in six languages, and show that our new approach improves accuracy by 1–17% (absolute) over CE (and 8–30% over EM), achieving to our knowledge the best results on this task to date. Our method, ...