ProDial – an annotated proactive dialogue act corpus for conversational assistants using crowdsourcing
- Proactive behaviour is an integral interaction concept of both human-human as well as human-computer cooperation. However, modelling proactive systems and appropriate interaction strategies are still an open quest. In this work, a parameterised and annotated dialogue corpus has been created. The corpus is based on human interactions with an autonomous agent embedded in a serious game setting. For modelling proactive dialogue behaviour, the agent was capable of selecting from four different proactive actions (None, Notification, Suggestion, Intervention) in order to serve as the user’s personal advisor in a sequential planning task. Data was collected online using crowdsourcing (308 participants) resulting in a total of 3696 system-user exchanges. Data was annotated with objective features as well as subjectively self-reported features for capturing the interplay between proactive behaviour and situational as well as user-dependent characteristics. The corpus is intended for building aProactive behaviour is an integral interaction concept of both human-human as well as human-computer cooperation. However, modelling proactive systems and appropriate interaction strategies are still an open quest. In this work, a parameterised and annotated dialogue corpus has been created. The corpus is based on human interactions with an autonomous agent embedded in a serious game setting. For modelling proactive dialogue behaviour, the agent was capable of selecting from four different proactive actions (None, Notification, Suggestion, Intervention) in order to serve as the user’s personal advisor in a sequential planning task. Data was collected online using crowdsourcing (308 participants) resulting in a total of 3696 system-user exchanges. Data was annotated with objective features as well as subjectively self-reported features for capturing the interplay between proactive behaviour and situational as well as user-dependent characteristics. The corpus is intended for building a user model for developing trustworthy proactive interaction strategies.…
Author: | Matthias KrausORCiDGND, Nicolas Wagner, Wolfgang Minker |
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URN: | urn:nbn:de:bvb:384-opus4-1013696 |
Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/101369 |
URL: | https://aclanthology.org/2022.lrec-1.339 |
ISBN: | 979-10-95546-72-6OPAC |
Parent Title (English): | Proceedings of the Thirteenth Language Resources and Evaluation Conference (LREC), 20-25 June 2022, Marseille, France |
Publisher: | European Language Resources Association (ELRA) |
Place of publication: | Paris |
Editor: | Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Peperidis |
Type: | Conference Proceeding |
Language: | English |
Year of first Publication: | 2022 |
Publishing Institution: | Universität Augsburg |
Release Date: | 2023/01/30 |
First Page: | 3164 |
Last Page: | 3173 |
Institutes: | Fakultät für Angewandte Informatik |
Fakultät für Angewandte Informatik / Institut für Informatik | |
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Menschzentrierte Künstliche Intelligenz | |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Licence (German): | CC-BY-NC 4.0: Creative Commons: Namensnennung - Nicht kommerziell (mit Print on Demand) |