“Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context”, Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov2019-01-09 (RNN, recurrent Transformers, GPT; backlinks; similar)⁠:

Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling.

We propose a novel neural architecture Transformer-​XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. 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 during evaluation.

Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwik8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens.

Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch.

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