Paper: Re-Ranking Models for Spoken Language Understanding

ACL ID E09-1024
Title Re-Ranking Models for Spoken Language Understanding
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

Spoken Language Understanding aims at mapping a natural language spoken sen- tence into a semantic representation. In the last decade two main approaches have been pursued: generative and discrimi- native models. The former is more ro- bust to overfitting whereas the latter is more robust to many irrelevant features. Additionally, the way in which these ap- proaches encode prior knowledge is very different and their relative performance changes based on the task. In this pa- per we describe a machine learning frame- work where both models are used: a gen- erative model produces a list of ranked hy- potheses whereas a discriminative model based on structure kernels and Support Vector Machines, re-ranks such list. We tested our approach on the MEDIA cor- pus (human-machine dialogs) and on a ...