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• W3213718944 abstract "Human dialogue contains evolving concepts, and speakers naturally associate multiple concepts to compose a response. However, current dialogue models with the seq2seq framework lack the ability to effectively manage concept transitions and can hardly introduce multiple concepts to responses in a sequential decoding manner. To facilitate a controllable and coherent dialogue, in this work, we devise a concept-guided non-autoregressive model (CG-nAR) for open-domain dialogue generation. The proposed model comprises a multi-concept planning module that learns to identify multiple associated concepts from a concept graph and a customized Insertion Transformer that performs concept-guided non-autoregressive generation to complete a response. The experimental results on two public datasets show that CG-nAR can produce diverse and coherent responses, outperforming state-of-the-art baselines in both automatic and human evaluations with substantially faster inference speed." @default.
• W3213718944 created "2021-11-22" @default.
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• W3213718944 date "2021-09-09" @default.
• W3213718944 modified "2023-09-23" @default.
• W3213718944 title "Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems" @default.
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• W3213718944 doi "https://doi.org/10.48550/arxiv.2109.04084" @default.
• W3213718944 hasPublicationYear "2021" @default.
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