Transformer-XL is a novel neural network architecture that improves language modeling by enabling natural language understanding beyond fixed-length contexts.
Transformer-XL addresses limitations of traditional Transformers in language modeling, specifically their inability to model dependencies longer than a fixed length and context fragmentation. It introduces two key techniques: a segment-level recurrence mechanism and a relative positional encoding scheme. The segment-level recurrence reuses representations from previous segments as an extended context, allowing information to flow across segment boundaries and increasing the maximum dependency length. The relative positional encoding scheme ensures coherence when reusing previous segments, making the recurrence mechanism viable. This approach uses fixed embeddings with learnable transformations, offering better generalization to longer sequences. Combined, these techniques allow Transformer-XL to process new segments without recomputation, leading to significant speed improvements during evaluation. Transformer-XL has achieved state-of-the-art results on various language modeling benchmarks, demonstrating improved performance in perplexity on both long and short sequences. It learns dependencies significantly longer than RNNs and vanilla Transformers and offers substantial speed increases during evaluation.
Integrations: Tensorflow, PyTorch
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