Generative and Discriminative Algorithms for Spoken Language Understanding - Archive ouverte HAL Access content directly
Conference Papers Year :

Generative and Discriminative Algorithms for Spoken Language Understanding

(1) , (1)
1

Abstract

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. Index Terms: spoken language understanding (SLU), conditional random fields (CRF), classifiers based sequence labeling, finite state transducers (FST).
Fichier principal
Vignette du fichier
i07_1605.pdf (106.63 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02949194 , version 1 (25-09-2020)

Identifiers

  • HAL Id : hal-02949194 , version 1

Cite

Christian Raymond, Giuseppe Riccardi. Generative and Discriminative Algorithms for Spoken Language Understanding. Interspeech 2007 - 8th Annual Conference of the International Speech Communication Association, Aug 2007, Anvers, Belgium. ⟨hal-02949194⟩
121 View
150 Download

Share

Gmail Facebook Twitter LinkedIn More