Graph Transformer for Graph-to-Sequence Learning (1911.07470v2)

Abstract: The dominant graph-to-sequence transduction models employ graph neural networks for graph representation learning, where the structural information is reflected by the receptive field of neurons. Unlike graph neural networks that restrict the information exchange between immediate neighborhood, we propose a new model, known as Graph Transformer, that uses explicit relation encoding and allows direct communication between two distant nodes. It provides a more efficient way for global graph structure modeling. Experiments on the applications of text generation from Abstract Meaning Representation (AMR) and syntax-based neural machine translation show the superiority of our proposed model. Specifically, our model achieves 27.4 BLEU on LDC2015E86 and 29.7 BLEU on LDC2017T10 for AMR-to-text generation, outperforming the state-of-the-art results by up to 2.2 points. On the syntax-based translation tasks, our model establishes new single-model state-of-the-art BLEU scores, 21.3 for English-to-German and 14.1 for English-to-Czech, improving over the existing best results, including ensembles, by over 1 BLEU.

Overview of "Graph Transformer for Graph-to-Sequence Learning"

The paper, "Graph Transformer for Graph-to-Sequence Learning," authored by Deng Cai and Wai Lam from The Chinese University of Hong Kong, introduces a novel approach to graph-to-sequence transduction via a Graph Transformer model. This model is proposed as an alternative to graph neural networks (GNNs), which, while state-of-the-art, have limitations in efficiently capturing global dependencies in graphs due to their local propagation design. This new architecture addresses these limitations by leveraging global communication mechanisms modeled entirely on the self-attention mechanism characteristic of Transformer architectures.

Key Contributions

1. Global Communication via Attention: Unlike GNNs, which update node representations by aggregating neighboring nodes, the Graph Transformer employs multi-head attention to facilitate global dependencies. This approach allows two nodes in a graph to interact directly regardless of their distance, mitigating the layer-dependence hindrance in capturing long-range dependencies.
2. Explicit Relation Encoding: The Graph Transformer incorporates explicit relation encoding, allowing specific node pair relations to inform attention computation dynamically. This avoids the dilution of graph structural information typical when treating a graph as fully connected.
3. Experimental Results: The authors report that their model achieves excellent performance on tasks such as AMR-to-text generation and syntax-based neural machine translation. Notably, the Graph Transformer outperforms state-of-the-art models by 2.2 BLEU points on the LDC2017T10 dataset for AMR-to-text conversion and by over 1 BLEU point in English-to-German and English-to-Czech translation tasks when compared to the best-performing models, including those using ensemble methods.

Theoretical and Practical Implications

The introduction of the Graph Transformer opens up several implications for both theoretical advancements and practical applications:

• Theoretical Implications: The work challenges the conventional local interactions in GNNs and highlights the potency of adopting a comprehensive attention-based framework for learning from graph-structured data. Its ability to efficiently handle long-distance dependencies without extensive layer stacking indicates a shift towards more holistic graph representations.
• Practical Implications: For NLP applications that heavily rely on graph representations, such as semantic parsing and syntactic translation, the proposed model offers a more efficient and effective mechanism for decoding graphs to sequences. This can lead to more accurate and nuanced output in text generation tasks.

Future Directions

The paper suggests several avenues for future research and development:

• Model Optimization: While the Graph Transformer sets new performance benchmarks, further optimization tailored to specific graph sizes and types may yield additional improvements.
• Extended Applications: The applicability of the Graph Transformer extends beyond AMR and syntax translation tasks. Exploration into other domains where graph structures are prevalent, such as social networks and biological data, may reveal the model's adaptability and potential beyond NLP.
• Fine-Tuning of Relation Encoding: Enhanced methods of encoding and utilizing graph relationships could be developed, potentially blending techniques from relational learning and graph embeddings to further improve model robustness and performance.

In summary, the paper presents a significant step forward in transforming how graph-structured data is processed in graph-to-sequence learning tasks, offering both innovative methodology and promising results across several benchmarks. Its approach could inspire further refinement and application in various domains of artificial intelligence research.