DINASTI : Dialogues with a Negotiating Appointment Setting Interface

• Main Authors: El Asri, Layla, Laroche, Romain, Pietquin, Olivier
• Other Authors: Georgia Tech Lorraine Metz, Ecole Nationale Supérieure des Arts et Metiers Metz-Georgia Institute of Technology Atlanta -Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté COMUE (UBFC)-Université Bourgogne Franche-Comté COMUE (UBFC), Orange Labs Issy les Moulineaux, France Télécom, Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2014
• Subjects: [INFO]Computer Science [cs]; [SPI]Engineering Sciences [physics]; Iceland
• Online Access: https://hal.inria.fr/hal-01107496

International audience This paper describes the DINASTI (DIalogues with a Negotiating Appointment SeTting Interface) corpus, which is composed of 1734dialogues with the French spoken dialogue system NASTIA (Negotiating Appointment SeTting InterfAce). NASTIA is a reinforcementlearning-based system. The DINASTI corpus was collected while the system was following a uniform policy. Each entry of the corpusis a system-user exchange annotated with 120 automatically computable features.The corpus contains a total of 21587 entries, with 385testers. Each tester performed at most five scenario-based interactions with NASTIA. The dialogues last an average of 10.82 dialogueturns, with 4.45 reinforcement learning decisions. The testers filled an evaluation questionnaire after each dialogue. The questionnaireincludes three questions to measure task completion. In addition, it comprises 7 Likert-scaled items evaluating several aspects of theinteraction, a numerical overall evaluation on a scale of 1 to 10, and a free text entry. Answers to this questionnaire are provided withDINASTI. This corpus is meant for research on reinforcement learning modelling for dialogue management.