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- W4283782122 abstract "Multi-turn conversations commonly face the challenges of adequately expressing emotions and enriching or expanding the content of the dialogues. The existing knowledge-based dialogue model can not avoid the problem of too rational response or ignoring appropriate expression of emotion. However, the precise emotions of one conversational bot are crucial to improve user satisfaction. In our work, we design an auto-emotion prediction for a chatting machine that considers six common fine-grained emotions and automatically assigns corresponding emotion for responses according to historical emotion lines and contexts. Moreover, we creatively fuse the multi-forms of knowledge dynamically to improve the response information, which can take advantage of both abundant unstructured latent knowledge in the documents and the information expansion capabilities of the structured knowledge graph. Notably, in the aspect of unstructured knowledge, a novel virtual knowledge base is constructed and a new delay updating algorithm is proposed. We present a new dialogue generation model, Automatically predicting emotion based dynamic multi-form knowledge fusion Conversation Generation (Apeak-CG). Both automatic and human evaluation results indicate the effectiveness of our method in terms of emotional coherence and informativeness." @default.
- W4283782122 created "2022-07-03" @default.
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- W4283782122 date "2022-09-01" @default.
- W4283782122 modified "2023-10-18" @default.
- W4283782122 title "Apeak-CG: Automatically predicting emotion based dynamic multi-form knowledge fusion conversation generation" @default.
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- W4283782122 doi "https://doi.org/10.1016/j.neucom.2022.06.105" @default.
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