BP-Transformer: Modelling Long-Range Context via Binary Partitioning

Abstract: 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 $O(k\cdot n\log (n/k))$ 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.

• The paper introduces a binary partitioning strategy that reduces self-attention’s quadratic complexity, enabling efficient long-range context modeling.
• It employs a hierarchical multi-scale analysis via a graph neural network perspective combined with relative position encoding for improved accuracy.
• Experimental evaluations reveal enhanced performance on text classification, language modeling, and machine translation compared to conventional Transformers.

Overview of BP-Transformer: Modeling Long-Range Context via Binary Partitioning

The paper introduces BP-Transformer (BPT), a novel model designed to address the quadratic complexity of self-attention mechanisms in traditional Transformer models, particularly when applied to long text sequences. By employing a binary partitioning strategy, BPT reduces computational complexity while maintaining the model's capacity to understand long-range dependencies.

Key Contributions

1. Binary Partitioning (BP) Strategy:
   • BPT implements a fine-to-coarse attention mechanism that partitions input sequences into hierarchical multi-scale spans using binary partitioning. This approach balances modeling capacity and computation complexity by generating $O(k \cdot n\log(n/k))$ connections rather than the $O(n^2)$ connections of traditional Transformers.
2. Graph Neural Network Perspective:
   • The architecture of BPT can be interpreted as a graph neural network where nodes represent multi-scale spans of the input sequence. This perspective facilitates the integration of hierarchical representations using Graph Self-Attention.
3. Relative Position Encoding:
   • BPT extends the concept of relative position encoding from sequences to hierarchical tree structures, which enhances the model's ability to capture positional bias effectively.

Experimental Evaluation

The paper provides an empirical evaluation of BPT across several NLP tasks, including text classification, machine translation, and language modeling. The results demonstrate that BPT consistently outperforms traditional Transformer models and some recent variants. Noteworthy results include:

• Text Classification:
  • On datasets like SST-5 and IMDB, BPT achieves higher accuracy than both the standard Transformer and Star Transformer models, validating its effectiveness for short and long text scenarios.
• Language Modeling:
  • BPT achieves state-of-the-art performance on character-level language modeling datasets such as Enwiki8 and Text8, with a reduced number of parameters compared to existing models.
• Machine Translation:
  • In both document-level and sentence-level translation tasks, BPT shows competitive BLEU scores, outperforming several conventional approaches like HAN-NMT and Transformer+Cache, particularly with moderate context lengths.

Practical and Theoretical Implications

The primary contribution of BPT lies in its ability to handle long sequences more efficiently than traditional Transformer models, through both computational and memory optimizations. This reduction in complexity enables the application of self-attention models to longer texts and potentially other domains requiring efficient long sequence processing such as time series prediction.

Theoretically, BPT bridges the gap between hierarchical and lightweight models, incorporating inductive biases that align more closely with natural language structures. This architecture might inspire further research into hybrid models that combine the strengths of both syntax-aware and efficient attention mechanisms.

Future Directions

BPT opens several avenues for further research and development. These include exploring the integration of syntactic and semantic information into its hierarchical structures and optimizing the GPU throughput for longer sequences. Additionally, future work might investigate the applicability of BPT to other sequence modeling tasks beyond NLP, taking advantage of its reduced computational demands and enhanced capacity for long-range context modeling.