A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation

Juraj Juraska, Panagiotis Karagiannis, Kevin Bowden, Marilyn Walker

Abstract
Natural language generation lies at the core of generative dialogue systems and conversational agents. We describe an ensemble neural language generator, and present several novel methods for data representation and augmentation that yield improved results in our model. We test the model on three datasets in the restaurant, TV and laptop domains, and report both objective and subjective evaluations of our best model. Using a range of automatic metrics, as well as human evaluators, we show that our approach achieves better results than state-of-the-art models on the same datasets.
Anthology ID:
N18-1014
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
152–162
Language:
URL:
https://aclanthology.org/N18-1014
DOI:
10.18653/v1/N18-1014
Bibkey:
Cite (ACL):
Juraj Juraska, Panagiotis Karagiannis, Kevin Bowden, and Marilyn Walker. 2018. A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 152–162, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation (Juraska et al., NAACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/N18-1014.pdf
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