160 citations · 332 across the 6 of their papers we have counts for
8 papers
Building a Conversational Agent Overnight with Dialogue Self-Play
Pararth Shah, Dilek Hakkani-Tür, Gokhan Tür +4
We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arb…
Scalable Multi-Domain Dialogue State Tracking
Abhinav Rastogi, Dilek Hakkani-Tur, Larry Heck
Dialogue state tracking (DST) is a key component of task-oriented dialogue systems. DST estimates the user's goal at each user turn given the interaction until then. State of the a…
Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning
Saurabh Kumar, Pararth Shah, Dilek Hakkani-Tur +1
We present a framework combining hierarchical and multi-agent deep reinforcement learning approaches to solve coordination problems among a multitude of agents using a semi-decentr…
End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning
Bing Liu, Gokhan Tur, Dilek Hakkani-Tur +2
In this paper, we present a neural network based task-oriented dialogue system that can be optimized end-to-end with deep reinforcement learning (RL). The system is able to track d…
Towards Zero-Shot Frame Semantic Parsing for Domain Scaling
Ankur Bapna, Gokhan Tur, Dilek Hakkani-Tur +1
State-of-the-art slot filling models for goal-oriented human/machine conversational language understanding systems rely on deep learning methods. While multi-task training of such…
Learning and Evaluating Musical Features with Deep Autoencoders
Mason Bretan, Sageev Oore, Doug Eck +1
In this work we describe and evaluate methods to learn musical embeddings. Each embedding is a vector that represents four contiguous beats of music and is derived from a symbolic…