arXiv:1603.09631
Data Collection for Interactive Learning through the Dialog
원문 초록
This paper presents a dataset collected from natural dialogs which enables to test the ability of dialog systems to learn new facts from user utterances throughout the dialog. This interactive learning will help with one of the most prevailing problems of open domain dialog system, which is the sparsity of facts a dialog system can reason about. The proposed dataset, consisting of 1900 collected dialogs, allows simulation of an interactive gaining of denotations and questions explanations from users which can be used for the interactive learning.
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An Empirical Study on Context Length for Open-Domain Dialog Generation
Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation
Open-Domain Dialog Evaluation using Follow-Ups Likelihood
Re2G: Retrieve, Rerank, Generate
GODEL: Large-Scale Pre-Training for Goal-Directed Dialog
CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI
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