Task-Oriented Dialogue Systems
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A User Simulator for Task-Completion Dialogues
Then, one can train reinforcement learning agents in an online fashion as they interact with the simulator. Once agents master the simulator, they may be deployed in a real environment to interact with humans, and continue to be trained online.
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems
HLT 2018 • google-research-datasets/simulated-dialogue
Popular methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pre-training models. To address this challenge, we propose a hybrid imitation and reinforcement learning method, with which a dialogue agent can effectively learn from its interaction with users by learning from human teaching and feedback.
#3 best model for
Dialogue State Tracking on Second dialogue state tracking challenge
DIALOGUE STATE TRACKING IMITATION LEARNING TASK-ORIENTED DIALOGUE SYSTEMS
Scalable Multi-Domain Dialogue State Tracking
• google-research-datasets/simulated-dialogue
State of the art approaches for state tracking rely on deep learning methods, and represent dialogue state as a distribution over all possible slot values for each slot present in the ontology. We introduce a novel framework for state tracking which is independent of the slot value set, and represent the dialogue state as a distribution over a set of values of interest (candidate set) derived from the dialogue history or knowledge.
DIALOGUE STATE TRACKING TASK-ORIENTED DIALOGUE SYSTEMS TRANSFER LEARNING
Global-Locally Self-Attentive Dialogue State Tracker
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules.
Key-Value Retrieval Networks for Task-Oriented Dialogue
Neural task-oriented dialogue systems often struggle to smoothly interface with a knowledge base. In this work, we seek to address this problem by proposing a new neural dialogue agent that is able to effectively sustain grounded, multi-domain discourse through a novel key-value retrieval mechanism.