Variational autoencoders (VAEs) are a neural network architecture broadly used in image generation (Doersch 2016). VAEs are neural network models that encode data from some domain and project it into a latent space (Doersch 2016). In doing so, the resulting encoding space goes from being a discrete distribution of vectors to a series of continuous manifolds. The latent space is subject to a Gaussian prior, giving the space some convenient properties for the distribution of said manifolds. Several strategies have been presented to try to disentangle said latent space to force each of their dimensions to have an interpretable meaning, for example, đť›˝-VAE, Factor-VAE, đť›˝-TCVAE. In this thesis, some previous VAE models for NaturalLanguage Processing (like Park and Lee (2021), and Bowman et al. (2015), where they finetune pretrained transformer models so they behave as VAEs, and where they used recurrent neural network language model to create a VAEs model that generates sentences in the continuous latent space, respectively) are combined with these disentangling techniques, to show if we can find any understandable meaning in the associated dimensions. The obtained results indicate that the techniques cannot be applied to text-based data without causing the model to suffer from posterior collapse.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-480308 |
| Date | January 2022 |
| Creators | GarcĂa de Herreros GarcĂa, Paloma |
| Publisher | Uppsala universitet, Institutionen för lingvistik och filologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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