A Comparison of Neural Models for Word Ordering

Eva Hasler, Felix Stahlberg, Marcus Tomalin, Adrià de Gispert, Bill Byrne


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.
Anthology ID:
W17-3531
Volume:
Proceedings of the 10th International Conference on Natural Language Generation
Month:
September
Year:
2017
Address:
Santiago de Compostela, Spain
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
208–212
Language:
URL:
https://www.aclweb.org/anthology/W17-3531
DOI:
10.18653/v1/W17-3531
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-3531.pdf