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• W3047872862 abstract "Natural language generation has become a fundamental task in dialogue systems. RNN-based natural response generation methods encode the dialogue context and decode it into a response. However, they tend to generate dull and simple responses. In this article, we propose a novel framework, called KAWA-DRG (Knowledge-aware Attentive Wasserstein Adversarial Dialogue Response Generation) to model conversation-specific external knowledge and the importance variances of dialogue context in a unified adversarial encoder-decoder learning framework. In KAWA-DRG, a co-attention mechanism attends to important parts within and among context utterances with word-utterance-level attention. Prior knowledge is integrated into the conditional Wasserstein auto-encoder for learning the latent variable space. The posterior and prior distribution of latent variables are generated and trained through adversarial learning. We evaluate our model on Switchboard, DailyDialog, In-Car Assistant, and Ubuntu Dialogue Corpus. Experimental results show that KAWA-DRG outperforms the existing methods." @default.
• W3047872862 created "2020-08-13" @default.
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• W3047872862 date "2020-05-28" @default.
• W3047872862 modified "2023-10-02" @default.
• W3047872862 title "Knowledge-aware Attentive Wasserstein Adversarial Dialogue Response Generation" @default.
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• W3047872862 doi "https://doi.org/10.1145/3384675" @default.
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