User Models , Dialog Structure , and Intentions inSpoken
نویسنده
چکیده
We outline how utterances in dialogs can be interpreted using a partial rst order logic. We exploit the capability of this logic to talk about the truth status of formulae to deene a notion of coherence between utterances and explain how this coherence relation can serve for the construction of AND/OR trees that represent the segmentation of the dialog. In a BDI model we formalize basic assumptions about dialog and cooperative behaviour of participants. These assumptions provide a basis for inferring speech acts from coherence relations between utterances and attitudes of dialog participants. Speech acts prove to be useful for determining dialog segments deened on the notion of completing expectations of dialog participants. Finally, we sketch how explicit segmentation signalled by cue phrases and performatives is covered by our dialog model. Der Beitrag beschreibt die Interpretation von Auuerungen mit Hilfe einer par-tiellen Logik erster Ordnung. Die FF ahigkeit dieser Logik, Aussagen uber den Wahrheitsstatus von Formeln zu treeen, dient der Formalisierung eines Kohh a-renzbegriis. Damit kann man die Struktur eines Dialogs als UND/ODER-Baum beschreiben. Dieser Ansatz wird in einem BDI-Modell um eine Formalisierung von Annahmen uber kooperatives Verhalten erweitert. Damit lassen sich { unter Einbezug der Auuerungskohh arenz { Sprechakte bestimmen, die n utzlich sind f ur die Dialogsegmentierung, die durch die Erf ullung von Erwartungen des Sprechers charakterisiert ist. Abschlieeend wird gezeigt, wie explizite Segmen-tierungssignale (cue phrases) von dem dargelegten Ansatz behandelt werden.
منابع مشابه
User Models, Dialog Structure, and Intentions in Spoken Dialog
We outline how utterances in dialogs can be interpreted using a partial rst order logic. We exploit the capability of this logic to talk about the truth status of formulae to deene a notion of coherence between utterances and explain how this coherence relation can serve for the construction of AND/OR trees that represent the segmentation of the dialog. In a BDI model we formalize basic assumpt...
متن کاملA Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems
User simulation is essential for generating enough data to train a statistical spoken dialogue system. Previous models for user simulation suffer from several drawbacks, such as the inability to take dialogue history into account, the need of rigid structure to ensure coherent user behaviour, heavy dependence on a specific domain, the inability to output several user intentions during one dialo...
متن کاملProbabilistic Ontology Trees for Belief Tracking in Dialog Systems
We introduce a novel approach for robust belief tracking of user intention within a spoken dialog system. The space of user intentions is modeled by a probabilistic extension of the underlying domain ontology called a probabilistic ontology tree (POT). POTs embody a principled approach to leverage the dependencies among domain concepts and incorporate corroborating or conflicting dialog observa...
متن کاملSpoken language interaction with model uncertainty: an adaptive human-robot interaction system
Spoken language is one of the most intuitive forms of interaction between humans and agents. Unfortunately, agents that interact with people using natural language often experience communication errors and do not correctly understand the user’s intentions. Recent systems have successfully used probabilistic models of speech, language, and user behavior to generate robust dialog performance in t...
متن کاملLearning User Intentions in Spoken Dialogue Systems
A common problem in spoken dialogue systems is finding the intention of the user. This problem deals with obtaining one or several topics for each transcribed, possibly noisy, sentence of the user. In this work, we apply the recent unsupervised learning method, Hidden Topic Markov Models (HTMM), for finding the intention of the user in dialogues. This technique combines two methods of Latent Di...
متن کامل