92 citations · 199 across the 5 of their papers we have counts for
5 papers
Multi-Domain Adversarial Learning for Slot Filling in Spoken Language Understanding
Bing Liu, Ian Lane
The goal of this paper is to learn cross-domain representations for slot filling task in spoken language understanding (SLU). Most of the recently published SLU models are domain-s…
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…
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models
Bing Liu, Tong Yu, Ian Lane +1
Dialog response selection is an important step towards natural response generation in conversational agents. Existing work on neural conversational models mainly focuses on offline…
Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Models
Bing Liu, Ian Lane
In this paper, we present a deep reinforcement learning (RL) framework for iterative dialog policy optimization in end-to-end task-oriented dialog systems. Popular approaches in le…
An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog
Bing Liu, Ian Lane
We present a novel end-to-end trainable neural network model for task-oriented dialog systems. The model is able to track dialog state, issue API calls to knowledge base (KB), and…