Papers › BP-Transformer: Modelling Long-Range Context via Binary Partitioning
BP-Transformer: Modelling Long-Range Context via Binary Partitioning
Zihao Ye, Qipeng Guo, Quan Gan, Xipeng Qiu, Zheng Zhang
The Transformer model is widely successful on many natural language processing tasks. However, the quadratic complexity of self-attention limit its application on long text. In this paper, adopting a fine-to-coarse attention mechanism on multi-scale spans via binary partitioning (BP), we propose BP-Transformer (BPT for short). BPT yields O(k·nlog(n/k)) connections where k is a hyperparameter to control the density of attention. BPT has a good balance between computation complexity and model capacity. A series of experiments on text classification, machine translation and language modeling shows BPT has a superior performance for long text than previous self-attention models. Our code, hyperparameters and CUDA kernels for sparse attention are available in PyTorch.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | Text8 | BP-Transformer - 12 Layers | Bit per Character (BPC) | 1.11 | #10 of 24 | Archive leaderboard | report |
| Language Modelling | enwik8 | BP-Transformer (12 layers) | Bit per Character (BPC) | 1.02 | #21 of 42 | Archive leaderboard | report |
| Language Modelling | enwik8 | BP-Transformer (12 layers) | Number of params | 38M | #21 of 42 | Archive leaderboard | report |
| Machine Translation | IWSLT2015 Chinese-English | BP-Transformer | BLEU | 19.84 | #1 of 1 | Archive leaderboard | report |
| Sentiment Analysis | IMDb | BP-Transformer + GloVe | Accuracy | 92.12 | #33 of 49 | Archive leaderboard | report |
| Sentiment Analysis | SST-5 Fine-grained classification | BP-Transformer + GloVe | Accuracy | 52.71 | #15 of 31 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: BP-Transformer
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