ACAT-G: An Interactive Learning Framework for Assisted Response Generation
DOI:
https://doi.org/10.1609/aaai.v35i18.18019Keywords:
Dialog System, Dialog Generation, Interactive LearningAbstract
In this paper, we introduce ACAT-G, an interactive dialogue learning framework that incorporates constant human feedback into fine-tuning language models in order to assist conditioned dialog generation. The system takes in a limited amount of input from a human and generates personalized response corresponding to the context of the conversation within natural dialog time-frame. By combining inspirations from online learning, reinforcement learning, and large scale language models, we expect this project to provide a foundation for human-in-the-loop conditional dialog generation tasks.Downloads
Published
2021-05-18
How to Cite
Lu, X., Sahay, S., Yu, Z., & Nachman, L. (2021). ACAT-G: An Interactive Learning Framework for Assisted Response Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16084-16086. https://doi.org/10.1609/aaai.v35i18.18019
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Section
AAAI Demonstration Track