Unsupervised concept-to-text generation with hypergraphs

Authors:
Ioannis Konstas;Mirella Lapata
Affiliations:
University of Edinburgh, Edinburgh;University of Edinburgh, Edinburgh
Venue:
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Year:
2012

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Abstract

Concept-to-text generation refers to the task of automatically producing textual output from non-linguistic input. We present a joint model that captures content selection ("what to say") and surface realization ("how to say") in an unsupervised domain-independent fashion. Rather than breaking up the generation process into a sequence of local decisions, we define a probabilistic context-free grammar that globally describes the inherent structure of the input (a corpus of database records and text describing some of them). We represent our grammar compactly as a weighted hypergraph and recast generation as the task of finding the best derivation tree for a given input. Experimental evaluation on several domains achieves competitive results with state-of-the-art systems that use domain specific constraints, explicit feature engineering or labeled data.