2021 · 72 citations · 29 references
ComputingComputer ScienceCurriculumArtificial IntelligenceMachine LearningNatural Language ProcessingConversational InterfacesConversational User InterfacesDialogue SystemsLanguage ModelingNatural Language GenerationText GenerationChatbotsCurriculum LearningDialog SystemsDialogue Response GenerationGenerative Response GenerationModel Performance EvaluationOpen-domain DialogueOpen-domain Text Generation
To build a high-quality open-domain chatbot, we introduce the effective training process of PLATO-2 via curriculum learning. There are two stages involved in the learning process. In the first stage, a coarse-grained generation model is trained to learn response generation under the simplified framework of oneto-one mapping. In the second stage, a finegrained generative model augmented with latent variables and an evaluation model are further trained to generate diverse responses and to select the best response, respectively. PLATO-2 was trained on both Chinese and English data, whose effectiveness and superiority are verified through comprehensive evaluations, achieving new state-of-the-art results.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma, Jimmy Ba · UvA-DARE (University of Amsterdam) · 2014 · 84.8K citations · Full text
DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2023 · 76.3K citations · Full text