Experiments with Generative Models for Dependency Tree Linearization

Abstract

We present experiments with generative models for linearization of unordered labeled syntactic dependency trees (Belz et al., 2011; Rajkumar and White, 2014). Our linearization models are derived from generative models for dependency structure (Eisner, 1996). We present a series of generative dependency models designed to capture successively more information about ordering constraints among sister dependents. We give a dynamic programming algorithm for computing the conditional probability of word orders given tree structures under these models. The models are tested on corpora of 11 languages using test-set likelihood, and human ratings for generated forms are collected for English. Our models benefit from representing local order constraints among sisters and from backing off to less sparse distributions, including distributions not conditioned on the head.