Transformer-XL

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• A Light Introduction to Transformer-XL | Elvis - Medium
• Natural Language Processing (NLP)
• Memory Networks
• Autoencoder (AE) / Encoder-Decoder

Combines the two leading architectures for language modeling:

1. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN) to handles the input tokens — words or characters — one by one to learn the relationship between them
2. Attention Mechanism/Model - Transformer Model to receive a segment of tokens and learns the dependencies between at once them using an attention mechanism.

Transformer-XL Explained: Combining Transformers and RNNs into a State-of-the-art Language Model; Summary of “Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context” | Rani Horev - Towards Data Science