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

Ref: https://arxiv.org/abs/1901.02860 The paper introduces Transformer-XL, a novel neural architecture for language modeling that overcomes the limitations of fixed-length contexts in standard Transformer models. It achieves this through a segment-level recurrence mechanism and a novel relative positional encoding scheme, enabling the capture of significantly longer-term dependencies. The resulting model demonstrates state-of-the-art performance on various language modeling benchmarks, exhibiting substantial speed improvements during evaluation and the ability to generate coherent long-form text. The authors present experimental results and ablation studies validating the effectiveness of their proposed techniques. They also offer insights into the attention mechanisms of the model.