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arXiv · 1708.01759

Referenceless Quality Estimation for Natural Language Generation

Ondřej Dušek·Jekaterina Novikova·Verena Rieser
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Abstract

Traditional automatic evaluation measures for natural language generation (NLG) use costly human-authored references to estimate the quality of a system output. In this paper, we propose a referenceless quality estimation (QE) approach based on recurrent neural networks, which predicts a quality score for a NLG system output by comparing it to the source meaning representation only. Our method outperforms traditional metrics and a constant baseline in most respects; we also show that synthetic data helps to increase correlation results by 21% compared to the base system. Our results are comparable to results obtained in similar QE tasks despite the more challenging setting.

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Ondřej Dušek, Jekaterina Novikova, Verena Rieser. 2017-08-05. Referenceless Quality Estimation for Natural Language Generation. https://arxiv.org/abs/1708.01759

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