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
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 208–212
- Language:
- URL:
- https://aclanthology.org/W17-3531
- DOI:
- 10.18653/v1/W17-3531
- Cite (ACL):
- Eva Hasler, Felix Stahlberg, Marcus Tomalin, Adrià de Gispert, and Bill Byrne. 2017. A Comparison of Neural Models for Word Ordering. In Proceedings of the 10th International Conference on Natural Language Generation, pages 208–212, Santiago de Compostela, Spain. Association for Computational Linguistics.
- Cite (Informal):
- A Comparison of Neural Models for Word Ordering (Hasler et al., INLG 2017)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W17-3531.pdf
- Code
- ehasler/tensorflow
- Data
- Penn Treebank