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arXiv · 1706.09433

Data-driven Natural Language Generation: Paving the Road to Success

Jekaterina Novikova·Ondřej Dušek·Verena Rieser
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Abstract

We argue that there are currently two major bottlenecks to the commercial use of statistical machine learning approaches for natural language generation (NLG): (a) The lack of reliable automatic evaluation metrics for NLG, and (b) The scarcity of high quality in-domain corpora. We address the first problem by thoroughly analysing current evaluation metrics and motivating the need for a new, more reliable metric. The second problem is addressed by presenting a novel framework for developing and evaluating a high quality corpus for NLG training.

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Jekaterina Novikova, Ondřej Dušek, Verena Rieser. 2017-06-28. Data-driven Natural Language Generation: Paving the Road to Success. https://arxiv.org/abs/1706.09433

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