Search Results for author:
Found 7 papers, 2 papers with code
Hexa: Self-Improving for Knowledge-Grounded Dialogue System
A common practice in knowledge-grounded dialogue generation is to explicitly utilize intermediate steps (e. g., web-search, memory retrieval) with modular approaches.
Effortless Integration of Memory Management into Open-Domain Conversation Systems
Open-domain conversation systems integrate multiple conversation skills into a single system through a modular approach.
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward
In order to resolve these issues, in this paper, we propose a learnable hash-based episodic count, which we name LECO, that efficiently performs as a task-specific intrinsic reward in hard exploration problems.
Selective Token Generation for Few-shot Natural Language Generation
Natural language modeling with limited training data is a challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its great generalization ability.
Selective Token Generation for Few-shot Language Modeling
Therefore, in this work, we develop a novel additive learning algorithm based on reinforcement learning (RL) for few-shot natural language generation (NLG) tasks.
Variational Intrinsic Control Revisited
In this paper, we revisit variational intrinsic control (VIC), an unsupervised reinforcement learning method for finding the largest set of intrinsic options available to an agent.
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning
In representing the MRRC problem as a sequential decision-making problem, we observe that each state can be represented as an extension of probabilistic graphical models (PGMs), which we refer to as random PGMs.
