| Challenge: | Experimental results demonstrate the generative superiority of SIVAE on both reconstruction and targeted syntactic evaluations. |
| Approach: | They propose a syntax-infused variational autoencoder that integrates sentences with their syntactic trees to improve the grammar of generated sentences. |
| Outcome: | The proposed model improves the grammar of generated sentences by integrating sentences with syntactic trees. |
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| Challenge: | Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. |
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Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders (2024.findings-naacl)
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Hierarchical Sketch Induction for Paraphrase Generation (2022.acl-long)
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Generative Text Modeling through Short Run Inference (2021.eacl-main)
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| Challenge: | Latent variable models for text capture global semantic and syntactic features when trained correctly. |
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Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders (2020.acl-main)
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| Challenge: | Existing conditional generation models cannot handle emerging conditions due to their joint end-to-end learning fashion. |
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| Challenge: | Variational autoencoders (VAEs) have received much attention as an end-to-end architecture for text generation with latent variables. |
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Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (2020.emnlp-main)
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Long and Diverse Text Generation with Planning-based Hierarchical Variational Model (D19-1)
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| Challenge: | Existing methods for data-to-text generation are insufficient to produce long and diverse texts. |
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