An Empirical Comparison Between N-gram and Syntactic Language Models for Word Ordering

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 or-der task. Our results show that the quality of automatically-parsed training data has a relatively small impact on syntactic mod-els. Both of syntactic and N-gram mod-els can benefit from large-scale raw text. Compared with N-gram models, syntac-tic models give overall better performance, but they require much more training time. In addition, the two models lead to differ-ent 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.