Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders (2024.findings-naacl)
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| Challenge: | Existing studies on syntactic injection in Variational AutoEncoders (VAEs) are limited to LSTM-based VAEs. |
| Approach: | They propose to use latent space separation techniques to inject syntactic information into Variational AutoEncoders (VAEs) using graph-based models. |
| Outcome: | The proposed end-to-end VAE architecture can improve the organisation of the latent space, alleviating the information loss occurring in standard VAE setups, and resulting in enhanced performances on language modelling and downstream generation tasks. |
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