Paper: A Generative Model for Parsing Natural Language to Meaning Representations

ACL ID D08-1082
Title A Generative Model for Parsing Natural Language to Meaning Representations
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

In this paper, we present an algorithm for learning a generative model of natural lan- guage sentences together with their for- mal meaning representations with hierarchi- cal structures. The model is applied to the task of mapping sentences to hierarchical rep- resentations of their underlying meaning. We introduce dynamic programming techniques for efficient training and decoding. In exper- iments, we demonstrate that the model, when coupled with a discriminative reranking tech- nique, achieves state-of-the-art performance when tested on two publicly available cor- pora. The generative model degrades robustly when presented with instances that are differ- ent from those seen in training. This allows a notable improvement in recall compared to previous models.