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- W3100236260 abstract "We propose a method to control the specificity of responses while maintaining the consistency with the utterances. We first design a metric based on pointwise mutual information, which measures the co-occurrence degree between an utterance and a response. To control the specificity of generated responses, we add the distant supervision based on the co-occurrence degree and a PMI-based word prediction mechanism to a sequence-to-sequence model. With these mechanisms, our model outputs the words with optimal specificity for a given specificity control variable. In experiments with open-domain dialogue corpora, automatic and human evaluation results confirm that our model controls the specificity of the response more sensitively than the conventional model and can generate highly consistent responses." @default.
- W3100236260 created "2020-11-23" @default.
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- W3100236260 date "2020-01-01" @default.
- W3100236260 modified "2023-10-14" @default.
- W3100236260 title "Consistent Response Generation with Controlled Specificity" @default.
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- W3100236260 doi "https://doi.org/10.18653/v1/2020.findings-emnlp.396" @default.
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