A Comparison of Neural Models for Word Ordering

Understand

We compare several language models for the word-ordering task and propose a new bag-to-sequence neural model based on attention-based 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.