User Modeling for Task Oriented Dialogues
arXiv:1811.04369
Abstract
We introduce end-to-end neural network based models for simulating users of task-oriented dialogue systems. User simulation in dialogue systems is crucial from two different perspectives: (i) automatic evaluation of different dialogue models, and (ii) training task-oriented dialogue systems. We design a hierarchical sequence-to-sequence model that first encodes the initial user goal and system turns into fixed length representations using Recurrent Neural Networks (RNN). It then encodes the dialogue history using another RNN layer. At each turn, user responses are decoded from the hidden representations of the dialogue level RNN. This hierarchical user simulator (HUS) approach allows the model to capture undiscovered parts of the user goal without the need of an explicit dialogue state tracking. We further develop several variants by utilizing a latent variable model to inject random variations into user responses to promote diversity in simulated user responses and a novel goal regularization mechanism to penalize divergence of user responses from the initial user goal. We evaluate the proposed models on movie ticket booking domain by systematically interacting each user simulator with various dialogue system policies trained with different objectives and users.
arXiv commentAccepted at SLT 2018
References in corpus (7)
- Adam: A Method for Stochastic Optimization
- Grammar as a Foreign Language
- A Hierarchical Neural Autoencoder for Paragraphs and Documents
- A Network-based End-to-End Trainable Task-oriented Dialogue System
- Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
- End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning
- Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Cited by in corpus (4)
- ConvLab: Multi-Domain End-to-End Dialog System Platform
- Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems
- Variational Reward Estimator Bottleneck: Learning Robust Reward Estimator for Multi-Domain Task-Oriented Dialog
- Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play