Paper: Underspecifying and Predicting Voice for Surface Realisation Ranking

ACL ID P11-1101
Title Underspecifying and Predicting Voice for Surface Realisation Ranking
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

This paper addresses a data-driven surface realisation model based on a large-scale re- versible grammar of German. We investigate the relationship between the surface realisa- tion performance and the character of the in- put to generation, i.e. its degree of underspec- ification. We extend a syntactic surface reali- sation system, which can be trained to choose among word order variants, such that the can- didate set includes active and passive variants. This allows us to study the interaction of voice and word order alternations in realistic Ger- man corpus data. We show that with an ap- propriately underspecified input, a linguisti- cally informed realisation model trained to re- generate strings from the underlying semantic representation achieves 91.5% accuracy (over a baseline of 82...