Graph-to-Sequence Learning using Gated Graph Neural Networks (1806.09835v1)

Abstract: Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on this setting obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and/or standard recurrent networks to achieve the best performance. In this work, we propose a new model that encodes the full structural information contained in the graph. Our architecture couples the recently proposed Gated Graph Neural Networks with an input transformation that allows nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work. Experimental results show that our model outperforms strong baselines in generation from AMR graphs and syntax-based neural machine translation.

• The paper presents a GGNN-based model for graph-to-sequence conversion, preserving structural information lost in linearization.
• It introduces a Levi graph transformation to reduce parameter complexity while enhancing edge label representation.
• The approach outperforms sequence-to-sequence baselines in AMR generation and syntax-based NMT, achieving higher BLEU scores.

Graph-to-Sequence Learning using Gated Graph Neural Networks

The paper "Graph-to-Sequence Learning using Gated Graph Neural Networks" by Daniel Beck, Gholamreza Haffari, and Trevor Cohn presents a novel approach to handling graph-based NLP tasks through a neural network architecture that leverages Gated Graph Neural Networks (GGNNs). The authors address common challenges in graph-to-sequence (g2s) learning by capitalizing on recent advances in neural network architectures while circumventing the information loss associated with linearization methodologies prevalent in earlier approaches.

Overview of the Proposed Methodology

The paper targets the transformation of directed acyclic graphs (DAGs) into sequences, a pervasive need in NLP applications such as Abstract Meaning Representation (AMR) graph generation and syntax-based neural machine translation (NMT). Traditional methods have grappled with the complexities of these graphs, often simplifying them into linear sequences and thereby disregarding critical structural information.

The authors propose a model that retains the graph's full structural essence by encoding it using GGNNs. GGNNs have the advantage of incorporating edge label information directly into the neural architecture via label-wise parameters. However, this advantage comes with a drawback: possible parameter explosion due to the potentially large vocabulary of edge labels. To mitigate this, the authors introduce a graph transformation technique—converting graphs into their "Levi graph" representation. This transformation turns edges into additional nodes, effectively decreasing the risk of parameter proliferation while enhancing the richness of the graph's representation in the GGNN.

Numerical Results and Analysis

The authors demonstrate empirical success on two specific g2s tasks: AMR graph-based sentence generation and syntax-enhanced NMT. The proposed model exhibits superior performance compared to baseline sequence-to-sequence (s2s) models in both tasks. It notably surpasses previous techniques by producing higher BLEU scores, a common metric for evaluating the quality of text that machine translation models produce.

For AMR generation, the g2s model outperformed strong s2s baselines without relying on additional linguistic heuristics like scope marking, a common crutch in previous studies. In syntax-based NMT, their model, even without the inclusion of recurrent components, managed to excel by integrating sequential connections into the dependency graph structures, further confirming the effectiveness of their approach.

Theoretical and Practical Implications

The deployment of GGNNs in g2s problems without resorting to recurrent components challenges the previously held notion that sequential encoders are indispensable. Furthermore, the successful application of Levi graph transformation demonstrates that disentangling edge labels and node labels can both conserve parameter economy and improve expressiveness.

On a theoretical level, the paper suggests new avenues for examining the interplay between graph transformations and neural architectures. Practically, the outcomes of this research provide a path forward for applications that handle graph-based data, suggesting that similar methodologies could be applied to other graph-like data structures in disparate fields such as computational biology or social network analysis.

Future Directions

This research opens up several prospective developments. One possibility includes dynamically adjusting the encoder layers to reflect varying graph complexities, potentially improving adaptability and efficiency. Another interesting direction concerns expanding the model to encode relationships between node and edge representations without imposing them on the same semantic space, which could further optimize performance by more accurately reflecting their distinct roles within graph structures.

In conclusion, the paper contributes significantly to the NLP field by presenting a robust and efficient model for graph-to-sequence learning. Its utilization of GGNNs and graph transformation techniques holds promise not only in enhancing current NLP applications but also in inspiring similar advances in related domains where structured data is prevalent.