Comparing stochastic approaches to spoken language understanding in multiple languages

Abstract : One of the first steps in building a spoken language understanding (SLU) module for dialogue systems is the extraction of flat concepts out of a given word sequence, usually provided by an automatic speech recognition (ASR) system. In this paper, six different modeling approaches are investigated to tackle the task of concept tagging. These methods include classical, well-known generative and discriminative methods like Finite State Transducers (FSTs), Statistical Machine Translation (SMT), Maximum Entropy Markov Models (MEMMs), or Support Vector Machines (SVMs) as well as techniques recently applied to natural language processing such as Conditional Random Fields (CRFs) or Dynamic Bayesian Networks (DBNs). Following a detailed description of the models, experimental and comparative results are presented on three corpora in different languages and with different complexity. The French MEDIA corpus has already been exploited during an evaluation campaign and so a direct comparison with existing benchmarks is possible. Recently collected Italian and Polish corpora are used to test the robustness and portability of the modeling approaches. For all tasks, manual transcriptions as well as ASR inputs are considered. Additionally to single systems, methods for system combination are investigated. The best performing model on all tasks is based on conditional random fields. On the MEDIA evaluation corpus, a concept error rate of 12.6% could be achieved. Here, additionally to attribute names, attribute values have been extracted using a combination of a rule-based and a statistical approach. Applying system combination using weighted ROVER with all six systems, the concept error rate (CER) drops to 12.0%.
Type de document :
Article dans une revue
IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2011, 19 (6), pp.1569-1583
Liste complète des métadonnées

Littérature citée [47 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00746965
Contributeur : Ist Rennes <>
Soumis le : mardi 30 octobre 2012 - 11:07:08
Dernière modification le : mercredi 11 avril 2018 - 02:00:50
Document(s) archivé(s) le : jeudi 31 janvier 2013 - 03:45:49

Fichier

plugin-05639034.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00746965, version 1

Citation

Stefan Hahn, Marco Dinarelli, Christian Raymond, Fabrice Lefèvre, Patrick Lehnen, et al.. Comparing stochastic approaches to spoken language understanding in multiple languages. IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2011, 19 (6), pp.1569-1583. 〈hal-00746965〉

Partager

Métriques

Consultations de la notice

543

Téléchargements de fichiers

349