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Address Limitation of RNN in NLP Problems by Using Transformer-XL
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Address Limitation of RNN in NLP Problems by Using Transformer-XL

Last Updated on July 25, 2023 by Editorial Team

Author(s): Edward Ma

Originally published on Towards AI.

Limitations of recurrent neural networks

Address Limitation of RNN in NLP Problems by Using Transformer-XL
Photo by Joe Gardner on Unsplash

Recurrent Neural Network (RNN) offers a way to learn a sequence of inputs. The drawback is that it is difficult to optimize due to vanishing gradient problem. Transformer (Al-Rfou et al., 2018) is introduced to overcome the limitation of RNN. By design, a fixed-length segment is defined to reduce resource consumption.

However, there is another problem that calls context fragmentation. If the input sequence is larger than pre-defined segment length, the input sequence needs to be separated and information cannot be captured across segments. Transformer-XL is introduced to overcome this limitation by Dai et al. (2019)

To… Read the full blog for free on Medium.

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