What is XL in Transformer?
The paper proposes a novel method called Transformer-XL (meaning extra long) for language modeling, which enables a Transformer architecture to learn longer-term dependency — via a recurrence mechanism — beyond a fixed length without disrupting temporal coherence.
What is the difference between Transformer and Transformer-XL?
As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation.
Why Transformer-XL?
Transformer-XL obtains new SoTA perplexity or bpc results on multiple datasets. Combining recurrence and relative positional encoding, it can model longer-term dependency than RNNs and vanilla Transformers, and reduce computational cost substantially during evaluation.
What is a feedback transformer?
A Feedback Transformer is a type of sequential transformer that exposes all previous representations to all future representations, meaning the lowest representation of the current timestep is formed from the highest-level abstract representation of the past.
Why do transformer models work?
Let’s take a look at how Transformer works. Transformer is a model that uses attention to boost the speed. The encoder’s inputs first flow through a self-attention layer. It helps the encoder look at other words in the input sentence as it encodes a specific word.
What does the extra long mean in transformer XL?
Transformer-XL (meaning extra long) is a Transformer architecture that introduces the notion of recurrence to the deep self-attention network. Instead of computing the hidden states from scratch for each new segment, Transformer-XL reuses the hidden states obtained in previous segments.
Who are the authors of the transformer XL model?
The Transformer-XL model was proposed in Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
Which is better transformer-XL or wikitext-103?
When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Transformer-XL uses relative sinusoidal positional embeddings. Padding can be done on the left or on the right.
How are hidden states used in transformer XL?
Instead of computing the hidden states from scratch for each new segment, Transformer-XL reuses the hidden states obtained in previous segments. The reused hidden states serve as memory for the current segment, which builds up a recurrent connection between the segments.