Estimating User Communication Styles for Spoken Dialogue Systems: Data

Erstveröffentlichung
2020-03-13Data creator
Miehle, Juliana
Feustel, Isabel
Hornauer, Julia
Minker, Wolfgang
Ultes, Stefan
Forschungsdaten
Faculties
Fakultät für Ingenieurwissenschaften, Informatik und PsychologieInstitutions
Institut für NachrichtentechnikConference
Language Resources and Evaluation Conference (LREC) 2020, 2020-05-13 - 2020-05-15, Marseille, Frankreich
Abstract
We present a neural network approach to estimate the communication style of spoken interaction, namely the stylistic variations elaborateness and directness, and investigate which type of input features to the estimator are necessary to achive good performance. First, we describe our annotated corpus of recordings in the health care domain and analyse the corpus statistics in terms of agreement, correlation and reliability of the ratings. We use this corpus to estimate the elaborateness and the directness of each utterance. We test different feature sets consisting of dialogue act features, grammatical features and linguistic features as input for our classifier and perform classification in two and three classes. Our classifiers use only features that can be automatically derived during an ongoing interaction in any spoken dialogue system without any prior annotation. Our results show that the elaborateness can be classified by only using the dialogue act and the amount of words contained in the corresponding utterance. The directness is a more difficult classification task and additional linguistic features in form of word embeddings improve the classification results. Afterwards, we run a comparison with a support vector machine and a recurrent neural network classifier.
Date created
2016/2019
EU Project uulm
KRISTINA / Knowledge-Based Information Agent with Social Competence and Human Interaction Capabilities / EC / H2020 / 645012
Subject headings
[GND]: Mensch-Maschine-Kommunikation | Kommunikations-Mix | Überwachtes Lernen[LCSH]: Dialogues | Computer networks | Supervised learning (Machine learning)
[Free subject headings]: Spoken Dialogue Systems | Dialogue Management | User Adaptation | Communication Styles
[DDC subject group]: DDC 004 / Data processing & computer science
Metadata
Show full item recordDOI & citation
Please use this identifier to cite or link to this item: http://dx.doi.org/10.18725/OPARU-26061
Miehle, Juliana et al. (2020): Estimating User Communication Styles for Spoken Dialogue Systems: Data. Open Access Repositorium der Universität Ulm und Technischen Hochschule Ulm. http://dx.doi.org/10.18725/OPARU-26061
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