113 citations · 454 across the 27 of their papers we have counts for
6 papers · 1 filter
Lifelong and Continual Learning Dialogue Systems
Sahisnu Mazumder, Bing Liu
Dialogue systems, commonly known as chatbots, have gained escalating popularity in recent times due to their wide-spread applications in carrying out chit-chat conversations with u…
Knowledge-Guided Exploration in Deep Reinforcement Learning
Sahisnu Mazumder, Bing Liu, Shuai Wang +4
This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of state-action permissibility (SAP). Tw…
Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings
Jorge A. Mendez, Alborz Geramifard, Mohammad Ghavamzadeh +1
Learning task-oriented dialog policies via reinforcement learning typically requires large amounts of interaction with users, which in practice renders such methods unusable for re…
KETOD: Knowledge-Enriched Task-Oriented Dialogue
Zhiyu Chen, Bing Liu, Seungwhan Moon +3
Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. Towards building a human-like assistant that can converse natura…
Continual Learning Based on OOD Detection and Task Masking
Gyuhak Kim, Sepideh Esmaeilpour, Changnan Xiao +1
Existing continual learning techniques focus on either task incremental learning (TIL) or class incremental learning (CIL) problem, but not both. CIL and TIL differ mainly in that…
AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation
Bing Liu, Sahisnu Mazumder, Eric Robertson +1
As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a self-…