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Found 8 papers, 5 papers with code
COLLIE: Systematic Construction of Constrained Text Generation Tasks
Text generation under constraints have seen increasing interests in natural language processing, especially with the rapidly improving capabilities of large language models.
DataMUX: Data Multiplexing for Neural Networks
In this paper, we introduce data multiplexing (DataMUX), a technique that enables deep neural networks to process multiple inputs simultaneously using a single compact representation.
Improving Dialog Systems for Negotiation with Personality Modeling
In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent's high-level strategy in negotiation tasks.
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear preferences, with the goal of enabling few-shot adaptation to new tasks.
Multi-Objective Reinforcement Learning
reinforcement-learning
End-to-End Refinement Guided by Pre-trained Prototypical Classifier
We propose imitation refinement, a novel approach to refine imperfect input patterns, guided by a pre-trained classifier incorporating prior knowledge from simulated theoretical data, such that the refined patterns imitate the ideal data.
Affordable On-line Dialogue Policy Learning
The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical.
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning
Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy.
On-line Dialogue Policy Learning with Companion Teaching
On-line dialogue policy learning is the key for building evolvable conversational agent in real world scenarios.
