An empirical comparison between N-gram and syntactic language models for word ordering

Abstract

Syntactic language models and N-gram language models have both been used in word ordering. In this paper, we give an empirical comparison between N-gram and syntactic language models on word order task. Our results show that the quality of automatically-parsed training data has a relatively small impact on syntactic models. Both of syntactic and N-gram models can benefit from large-scale raw text. Compared with N-gram models, syntactic models give overall better performance, but they require much more training time. In addition, the two models lead to different error distributions in word ordering. A combination of the two models integrates the advantages of each model, achieving the best result in a standard benchmark.