Paper: Re-Ranking Models Based-on Small Training Data for Spoken Language Understanding

ACL ID D09-1112
Title Re-Ranking Models Based-on Small Training Data for Spoken Language Understanding
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

The design of practical language applica- tions by means of statistical approaches requires annotated data, which is one of the most critical constraint. This is par- ticularly true for Spoken Dialog Systems since considerably domain-specific con- ceptual annotation is needed to obtain ac- curate Language Understanding models. Since data annotation is usually costly, methods to reduce the amount of data are needed. In this paper, we show that bet- ter feature representations serve the above purpose and that structure kernels pro- vide the needed improved representation. Given the relatively high computational cost of kernel methods, we apply them to just re-rank the list of hypotheses provided by a fast generative model. Experiments with Support Vector Machines and differ- ent kernels on t...