Jointly Optimizing Diversity and Relevance in Neural Response Generation (N19-1)
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| Challenge: | Recent neural conversation models often generate bland and generic responses . however, the improvement often comes at the cost of decreased relevance . |
| Approach: | They propose a spacefusion model to jointly optimize diversity and relevance that fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms. |
| Outcome: | The proposed model improves diversity and relevance compared to baselines in both diversity and diversity. |
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| Challenge: | Neural network-based sequence-to-sequence models suffer from low diversity in open-domain dialogue generation. |
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| Challenge: | Neural encoder-decoder models tend to generate meaningless and generic responses regardless of what the input text is. |
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