Diversifying Dialogue Generation with Non-Conversational Text
Hui Su, Xiaoyu Shen, Sanqiang Zhao, Xiao Zhou, Pengwei Hu, Randy Zhong, Cheng Niu, Jie Zhou
Abstract
Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the lowdiversity problem when it comes to opendomain dialogue generation. As bland and generic utterances usually dominate the frequency distribution in our daily chitchat, avoiding them to generate more interesting responses requires complex data filtering, sampling techniques or modifying the training objective.
Topics & Concepts
Computer sciencePerspective (graphical)Natural language processingContext (archaeology)Artificial intelligenceRelevance (law)Sequence (biology)Domain (mathematical analysis)Range (aeronautics)Speech recognitionMathematicsLawPolitical scienceGeneticsPaleontologyMathematical analysisMaterials scienceBiologyComposite materialTopic ModelingSpeech and dialogue systemsNatural Language Processing Techniques