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E-commerce pre-sales dialogue aims to understand and elicit user needs and preferences for the items they are seeking so as to provide appropriate recommendations.
Language models are few-shot learners
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Attention is all you need
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Self-Attentive Sequential Recommendation
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Named Entity Recognition Using BERT BiLSTM CRF for Chinese Electronic Health Records
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BERT: Pre-training of deep bidirectional transformers for language understanding
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Portuguese Named Entity Recognition using BERT-CRF
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The JDDC corpus: A large-scale multi-turn Chinese dialogue dataset for E-commerce customer service
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
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Rethinking supervised learning and reinforcement learning in task-oriented dialogue systems
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Towards conversational recommendation over multi-type dialogs
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Conversational contextual bandit: Algorithm and application
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Variational reasoning about user preferences for conversational recommendation
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua. 2021 · 2021
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A Survey on Conversational Recommender Systems
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Learning neural templates for recommender dialogue system
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CR-walker: Tree-structured graph reasoning and dialog acts for conversational recommendation
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Cpt: A pre-trained unbalanced transformer for both chinese language understanding and generation
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Improving conversational recommender system by pretraining billion-scale knowledge graph
Chi-Man Wong, Fan Feng, Wen Zhang, Chi-Man Vong, Hui Chen, Yichi Zhang, Peng He, Huan Chen, Kun Zhao, and Huajun Chen. 2021 · 2021
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Nan Zhao, Haoran Li, Youzheng Wu, Xiaodong He, and Bowen Zhou. 2021 · 2021
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C²-crs: Coarse-to-fine contrastive learning for conversational recommender system
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Efficient and effective text encoding for chinese llama and alpaca
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Is chatgpt good at search? investigating large language models as re-ranking agent
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Llama: Open and efficient foundation language models
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Rethinking the evaluation for conversational recommendation in the era of large language models
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A survey of large language models
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