Language models (LM) have grown with non-stop in the last decade, from sequence-to-sequence architectures to the state-of-the-art and utter attention-based Transformers. In this work, we demonstrate how the inclusion of deep generative models within BERT can bring more versatile models, able to impute missing/noisy words with richer text or even improve BLEU score. More precisely, we use a Gaussian Mixture Variational Autoencoder (GMVAE) as a regularizer layer and prove its effectiveness not only in Transformers but also in the most relevant encoder-decoder based LM, seq2seq with and without attention.
@article{arxiv.2108.10764,
title = {Regularizing Transformers With Deep Probabilistic Layers},
author = {Aurora Cobo Aguilera and Pablo Martínez Olmos and Antonio Artés-Rodríguez and Fernando Pérez-Cruz},
journal= {arXiv preprint arXiv:2108.10764},
year = {2021}
}