Papers by Tom Pelsmaeker
Effective Estimation of Deep Generative Language Models (2020.acl-main)
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| Challenge: | Existing techniques for parameterisation of probabilistic models by deep neural networks are difficult to use in language modelling due to posterior collapse. |
| Approach: | They propose to use variational auto-encoder to estimate probabilistic models of language by deep neural networks. |
| Outcome: | The proposed model performs reasonably well given enough resources, but a favourite can be named based on convenience. |