A comparison of neural models for word ordering

Abstract

We compare several language models for the word-ordering task and propose a new bagto-sequence neural model based on attentionbased sequence-to-sequence models. We evaluate the model on a large German WMT data set where it significantly outperforms existing models. We also describe a novel search strategy for LM-based word ordering and report results on the English Penn Treebank. Our best model setup outperforms prior work both in terms of speed and quality.