Spoken Language Understanding (SLU) for conversational systems (SDS)
aims at extracting concept and their relations from spontaneous speech.
Previous approaches to SLU have modeled concept relations as stochastic
semantic networks ranging from generative approach to discriminative.
As spoken dialog systems complexity increases, SLU needs to perform
understanding based on a richer set of features ranging from a-priori
knowledge, long dependency, dialog history, system belief, etc. This
paper studies generative and discriminative approaches to modeling
the sentence segmentation and concept labeling. We evaluate algorithms
based on Finite State Transducers (FST) as well as discriminative
algorithms based on Support Vector Machine sequence classifier based
and Conditional Random Fields (CRF). We compare them in terms of
concept accuracy, generalization and robustness to annotation ambiguities.
We also show how non-local non-lexical features (e.g. a-priori knowledge)
can be modeled with CRF which is the best performing algorithm across
tasks. The evaluation is carried out on two SLU tasks of different
complexity, namely ATIS and MEDIA corpora.
Mendeley saves you time finding and organizing research
Choose a citation style from the tabs below