Difference between revisions of "Transformer-XL"
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| − | combines the two leading architectures for language | + | 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. [http://towardsdatascience.com/transformer-xl-explained-combining-transformers-and-rnns-into-a-state-of-the-art-language-model-c0cfe9e5a924 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] | ||
Revision as of 16:12, 19 January 2019
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- A Light Introduction to Transformer-XL | Elvis - Medium
- Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)
- Natural Language Processing (NLP)
- Memory Networks
- Attention Mechanism/Model - Transformer Model
- 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
