Shypd.aiThe largest open index of AI toolsNeuralDialogPapers is a Research & Education tool that provides a curated list of deep learning models for dialog systems. It summarizes advancements in the field, maintained by Tiancheng Zhao at CMU, and is open for community contributions.
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NeuralDialogPapers offers a comprehensive, curated list of research papers focusing on deep learning models specifically designed for dialog systems. This resource, maintained by Tiancheng Zhao from LTI, CMU, serves as a valuable summary of the latest advancements and methodologies in the field. It categorizes papers across various aspects of dialog systems, including task bots, multidomain adaptation, user simulators, reinforcement learning, adversarial chat bots, retrieval methods, rich dialog context, diversity, and interpretability. The platform encourages community contributions, allowing researchers and practitioners to add missing papers and keep the resource up-to-date, making it a dynamic and collaborative knowledge base for anyone interested in neural dialog research.
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