Claire Gardent gives a Keynote at SLSP 2014 on Natural Language Syntax and Data-to-Text Generation

Date : 14 October 2014, SLSP 2014

Speaker: Claire Gardent

Abstract :
With the development of the web of data, recent statistical, data-to-text generation approaches have focused on mapping data (e.g., database records or knowledge-base (KB) triples) to natural language. In contrast to previous grammar-based approaches, this more recent work systematically eschews syntax and learns a direct mapping between meaning representations and natural language. By contrast, I argue that an explicit model of syntax can help support NLG in several ways. Based on case studies drawn from KB-to-text generation, I show that syntax can be used to support supervised training with little training data; to ensure domain portability; and to improve statistical hypertagging.


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