This paper proposes a data-driven method for concept-to-text generation, the task of automatically producing textual output from non-linguistic input. A key insight in our approach is to reduce the tasks of content selection (“what to say”) and surface realization (“how to say”) into a common parsing problem. We define a probabilistic context-free grammar that describes the structure of the in-put (a corpus of database records and text describing some of them) and represent it compactly as a weighted hypergraph. The hyper-graph structure encodes exponentially many derivations, which we rerank discriminatively using local and global features. We propose a novel decoding algorithm for finding the best scoring derivation and generating in this setting. Experimental evaluation on the A TIS domain shows that our model outperforms a competitive discriminative system both using BLEU and in a judgment elicitation study.
|Title of host publication||The 50th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference|
|Publisher||Association for Computational Linguistics|
|Number of pages||10|
|Publication status||Published - 2012|