1 citations · 1 across the 4 of their papers we have counts for
4 papers
Mem-α: Learning Memory Construction via Reinforcement Learning
Yu Wang, Ryuichi Takanobu, Zhiqi Liang +4
Large language model (LLM) agents are constrained by limited context windows, necessitating external memory systems for long-term information understanding. Current memory-augmente…
Manual-Guided Dialogue for Flexible Conversational Agents
Ryuichi Takanobu, Hao Zhou, Yankai Lin +3
How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be two critical issues in building a task-oriented dialogue system. In th…
End-to-End Task-Oriented Dialog Modeling with Semi-Structured Knowledge Management
Silin Gao, Ryuichi Takanobu, Antoine Bosselut +1
Current task-oriented dialog (TOD) systems mostly manage structured knowledge (e.g. databases and tables) to guide the goal-oriented conversations. However, they fall short of hand…
CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation
Wenchang Ma, Ryuichi Takanobu, Minlie Huang
Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate re…