Be More with Less: Hypergraph Attention Networks for Inductive Text Classification (2011.00387v1)

Abstract: Text classification is a critical research topic with broad applications in natural language processing. Recently, graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are: (1) unable to capture high-order interaction between words; (2) inefficient to handle large datasets and new documents. To address those issues, in this paper, we propose a principled model -- hypergraph attention networks (HyperGAT), which can obtain more expressive power with less computational consumption for text representation learning. Extensive experiments on various benchmark datasets demonstrate the efficacy of the proposed approach on the text classification task.

• The paper introduces HyperGAT, which uses hypergraph structures to capture high-order word interactions for more nuanced text classification.
• It employs document-level hypergraph construction with sequential and semantic hyperedges combined with a dual-layer attention mechanism to enhance representational power.
• Empirical evaluations on multiple datasets reveal that HyperGAT outperforms traditional GNN models in accuracy and computational efficiency.

Hypergraph Attention Networks for Inductive Text Classification

The paper presents a novel approach to the challenging task of text classification in natural language processing through the introduction of Hypergraph Attention Networks (HyperGAT). This method aims to address two significant limitations of traditional graph neural networks (GNNs) when applied to text classification: their inability to capture high-order interactions between words and the inefficiency they demonstrate in managing large datasets efficiently.

Motivation and Limitations of Existing Methods

Text classification serves as a foundational task within NLP, with applications in areas such as sentiment analysis, topic labeling, and medical diagnosis. Historically, deep learning models like CNNs and RNNs have excelled in this area due to their ability to capture sequential word relationships. However, the emergence of GNNs has shifted some focus towards leveraging graph-based structures to model text by encoding long-distance word interactions in a corpus-level graph. Despite their apparent advantages, these traditional GNN-based methods often fall short in practical applications for two primary reasons:

1. Expressive Power: Traditional GNN approaches primarily focus on dyadic (pairwise) interactions, neglecting the more complex multi-way interactions (triadic, tetradic) commonplace in language. This limitation impairs the model’s ability to accurately capture nuanced relationships, often leading to misinterpretations, as exemplified by idiomatic expressions.
2. Computational Complexity: The memory-intensive nature of traditional GNNs, owing to their construction of large corpus-level graphs, alongside their transductive learning style (requiring access to test data during training), exacerbates inefficiencies, particularly as datasets grow in size or when new data is continually added.

Hypergraph Attention Networks (HyperGAT)

To overcome these limitations, the HyperGAT model is proposed, with a fundamental shift from simple graphs to hypergraphs. Unlike conventional graphs where edges connect two nodes, hypergraphs allow for hyperedges connecting multiple nodes, thus more naturally capturing high-order word interactions.

Key Components of HyperGAT

1. Document-Level Hypergraph Construction: HyperGAT models each document within a hypergraph framework, whereby each hyperedge connects multiple words. Rationale for hyperedge formation in HyperGAT involves:
   • Sequential Hyperedges: Modeled by using sentences to encapsulate sequential context.
   • Semantic Hyperedges: Formulated through topic modeling (e.g., LDA) to capture semantic relationships by connecting top-probability words within each topic.
2. Dual Attention Mechanism: HyperGAT employs a dual-layer attention mechanism to enhance expressiveness:
   • Node-Level Attention: Determines the importance of nodes within hyperedges, enabling fine-grained cross-word interactions.
   • Edge-Level Attention: Evaluates the significance of hyperedges relative to each node, emphasizing informative contextual links within the document.

Experimental Results

Experimental evaluations on five distinct datasets (20-Newsgroups, Reuters, Ohsumed, Movie Review) demonstrate HyperGAT’s superior text classification performance over existing models, particularly excelling in scenarios where high-order interactions are pivotal. Moreover, HyperGAT exhibits significant computational efficiency, as evidenced by reduced GPU memory consumption compared to traditional transductive GNN models.

Implications and Future Directions

The introduction of HyperGAT is a significant stride towards more accurate and efficient text representation learning. Its capacity to generalize to unseen documents also marks a distinct shift towards inductive approaches, which are crucial in dynamic, real-world settings where data continually evolves. Future work could extend HyperGAT by incorporating additional contextual hyperedges (e.g., syntactic relations) and exploring applications beyond text classification into further NLP tasks. Additionally, the fusion of HyperGAT with other state-of-the-art representation frameworks (e.g., transformers) could unlock even more powerful insights into language processing.