Transformer-XL: Attentive Language Models beyond a Fixed-Length Context

@article{Dai2019TransformerXLAL,
  title={Transformer-XL: Attentive Language Models beyond a Fixed-Length Context},
  author={Zihang Dai and Zhilin Yang and Yiming Yang and Jaime G. Carbonell and Quoc V. Le and Ruslan Salakhutdinov},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.02860}
}
Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. [] Key Method Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. 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…

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