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2009
ACL Anthology

Re-Ranking Models for Spoken Language Understanding

9 years 11 months ago
Re-Ranking Models for Spoken Language Understanding
Spoken Language Understanding aims at mapping a natural language spoken sentence into a semantic representation. In the last decade two main approaches have been pursued: generative and discriminative models. The former is more robust to overfitting whereas the latter is more robust to many irrelevant features. Additionally, the way in which these approaches encode prior knowledge is very different and their relative performance changes based on the task. In this paper we describe a machine learning framework where both models are used: a generative model produces a list of ranked hypotheses whereas a discriminative model based on structure kernels and Support Vector Machines, re-ranks such list. We tested our approach on the MEDIA corpus (human-machine dialogs) and on a new corpus (human-machine and humanhuman dialogs) produced in the European LUNA project. The results show a large improvement on the state-of-the-art in concept segmentation and labeling.
Marco Dinarelli, Alessandro Moschitti, Giuseppe Ri
Added 24 Nov 2009
Updated 24 Nov 2009
Type Conference
Year 2009
Where EACL
Authors Marco Dinarelli, Alessandro Moschitti, Giuseppe Riccardi
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