3 accepted papers
Training dialogue agents requires a large number of interactions with users: agents have no idea about which responses are bad among a lengthy dialogue. In this paper, we propose loop-clipping policy optimisation (LCPO) to eliminate useless responses. LCPO consists of two stages: loop clipping and a…
By interacting with human and learning from reward signals, reinforcement learning is an ideal way to build conversational AI. Concerning the expenses of real-users' responses, improving sample-efficiency has been the key issue when applying reinforcement learning in real-world spoken dialogue syste…
In this paper we describe the use of curiosity rewards for dialogue policy learning of goal oriented dialogues via reinforcement learning. Using curiosity improves state-action space exploration and helps overcome reward sparsity. Additionally, for goal oriented dialogues it makes sense to perform i…