Statistical User Simulation for Spoken Dialogue Systems: What for, Which Data, Which Future?
Code (0)
등록된 구현이 없습니다.
Tasks
Dialogue ManagementSpoken Dialogue SystemsUser SimulationSimilar Papers 제목 키워드 기반
A 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 int…
DecoderDialogue State TrackingSpoken Dialogue SystemsUser SimulationRetico: An incremental framework for spoken dialogue systems
In this paper we present the newest version of retico - a python-based incremental dialogue framework to create state-of-the-art spoken dialogue systems and simulations. Retico provides a range of incremental modules tha…
Spoken Dialogue SystemsTranslationReward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
Statistical spoken dialogue systems have the attractive property of being able to be optimised from data via interactions with real users. However in the reinforcement learning paradigm the dialogue manager (agent) often…
Reinforcement LearningSpoken Dialogue SystemsLearning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems
To train a statistical spoken dialogue system (SDS) it is essential that an accurate method for measuring task success is available. To date training has relied on presenting a task to either simulated or paid users and …
Reinforcement LearningSpoken Dialogue SystemsSpokenUS: A Spoken User Simulator for Task-Oriented Dialogue
Robust task-oriented spoken dialogue agents require exposure to the full diversity of how people interact through speech. Building spoken user simulators that address this requires large-scale spoken task-oriented dialog…