4 papers
Enhancing Persuasive Dialogue Agents by Synthesizing Cross-Disciplinary Communication Strategies
Shinnosuke Nozue, Yuto Nakano, Yotaro Watanabe +4
Current approaches to developing persuasive dialogue agents often rely on a limited set of predefined persuasive strategies that fail to capture the complexity of real-world intera…
Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications
Chihiro Yano, Akihiko Fukuchi, Shoko Fukasawa +2
Prior work on multilingual sentence embedding has demonstrated that the efficient use of natural language inference (NLI) data to build high-performance models can outperform conve…
Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives
Hideyuki Tachibana, Mocho Go, Muneyoshi Inahara +2
Diffusion generative models have emerged as a new challenger to popular deep neural generative models such as GANs, but have the drawback that they often require a huge number of n…
Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings
Takuya Hiraoka, Masaaki Tsuchida, Yotaro Watanabe
This paper is the first attempt to learn the policy of an inquiry dialog system (IDS) by using deep reinforcement learning (DRL). Most IDS frameworks represent dialog states and di…