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

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

16 May 2018NAACL 2018 6arXiv:1805.06553archive 2025-07-28

Juraj Juraska, Panagiotis Karagiannis, Kevin K. Bowden, Marilyn A. Walker

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.

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Tasks

Data-to-Text GenerationText Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Data-to-Text Generation E2E NLG Challenge Slug BLEU 66.19 #4 of 11 Archive leaderboard report
Data-to-Text Generation E2E NLG Challenge Slug METEOR 44.54 #4 of 11 Archive leaderboard report
Data-to-Text Generation E2E NLG Challenge Slug NIST 8.6130 #4 of 11 Archive leaderboard report
Data-to-Text Generation E2E NLG Challenge Slug ROUGE-L 67.72 #4 of 11 Archive leaderboard report

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