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2022 NAACL NAACL 2022

Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition

Abstract

AbstractWe propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot. Our information acquisition-oriented dialogue system employs a novel adaptation of reinforced self-play so that the system can be transferred to various domains without in-domain dialogue data, and can carry out conversations both informative and attentive to users.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Natural Language Processing and Reinforcement Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Pengshan Cai , Hui Wan , Fei Liu , Mo Yu , Hong Yu , Sachindra Joshi

Topics

Artificial Intelligence > Core AI > Agent Systems Natural Language Processing > Generation > Dialogue Systems Machine Learning > Learning Types > Reinforcement Learning Natural Language Processing > Applications > Dialogue Systems Reinforcement Learning > Applications > Multi-Agent Systems

Keywords

reinforcement learning conversational ai information acquisition knowledge acquisition dialogue system
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