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- W2803299344 abstract "Memory augmented encoder-decoder framework has achieved promising progress for natural language generation tasks. Such frameworks enable a decoder to retrieve from a memory during generation. However, less research has been done to take care of the memory contents from different sources, which are often of heterogeneous formats. In this work, we propose a novel attention mechanism to encourage the decoder to actively interact with the memory by taking its heterogeneity into account. Our solution attends across the generated history and memory to explicitly avoid repetition, and introduce related knowledge to enrich our generated sentences. Experiments on the answer sentence generation task show that our method can effectively explore heterogeneous memory to produce readable and meaningful answer sentences while maintaining high coverage for given answer information." @default.
- W2803299344 created "2018-06-01" @default.
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- W2803299344 date "2018-01-01" @default.
- W2803299344 modified "2023-09-26" @default.
- W2803299344 title "Natural Answer Generation with Heterogeneous Memory" @default.
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- W2803299344 doi "https://doi.org/10.18653/v1/n18-1017" @default.
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