Regularizing transformers with deep probabilistic layers.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Deep Learning

Background:

• Transformer architectures have dominated recent advancements in language models (LM).
• Regularization techniques are crucial for improving LM performance but remain underexplored in Transformer structures.
• Deep generative models offer potential for enhancing LM regularization.

Purpose of the Study:

• To investigate the effectiveness of a Gaussian Mixture Variational Autoencoder (GMVAE) as a regularization layer within Transformer-based language models.
• To analyze the impact of GMVAE placement depth on model performance.
• To demonstrate the versatility and improved generalization of Transformer models augmented with GMVAE.

Main Methods:

• Implementation of a Gaussian Mixture Variational Autoencoder (GMVAE) as a regularization layer.
• Integration of the GMVAE into Transformer architectures like BERT, RoBERTa, and XLM-R.
• Evaluation of model performance on tasks including SST-2 and TREC, focusing on generalization and imputation scores.

Main Results:

• The inclusion of GMVAE as a regularizer significantly enhances the versatility of Transformer-based LMs.
• Models incorporating GMVAE demonstrate improved generalization capabilities across various tasks.
• GMVAE integration leads to superior performance in text imputation, including handling missing or noisy words with richer output.

Conclusions:

• Deep generative models, specifically GMVAE, can be effectively employed as regularizers in Transformer architectures.
• GMVAE regularization offers a promising approach to developing more robust and capable language models.
• This method provides a pathway to richer text generation and improved performance on challenging NLP tasks.