학술논문
A Comparison of Neural Models for Word Ordering
Document Type
Working Paper
Source
Subject
Language
Abstract
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.
Comment: Accepted for publication at INLG 2017
Comment: Accepted for publication at INLG 2017