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InsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem?

Fang Wu, Stan Z. Li

TL;DR

This work tackles the sentence insertion problem by reformulating SI as a directed sentence graph and introducing InsertGNN, a global-local fused Graph Neural Network designed to capture both cross-sentence coherence and local cueing. The model combines a Global Graph Attention Network for global context with a Local Graph Network that focuses on nearby sentence interactions, and fuses these signals to predict insertion points with a triple BCE loss. On TOEFL data, InsertGNN achieves up to about 71.5% accuracy, rivaling average human performance, and demonstrates cross-domain transfer to arXiv with competitive TOEFL results, outperforming several baselines and some LLM prompts. The results validate the graph-based SI paradigm and indicate strong potential for cross-domain generalization, while also highlighting dataset limitations and avenues for future work with larger datasets and more capable LLMs.

Abstract

The integration of sentences poses an intriguing challenge within the realm of NLP, but it has not garnered the attention it deserves. Existing methods that focus on sentence arrangement, textual consistency, and question answering are inadequate in addressing this issue. To bridge this gap, we introduce InsertGNN, which conceptualizes the problem as a graph and employs a hierarchical Graph Neural Network (GNN) to comprehend the interplay between sentences. Our approach was rigorously evaluated on a TOEFL dataset, and its efficacy was further validated on the expansive arXiv dataset using cross-domain learning. Thorough experimentation unequivocally establishes InsertGNN's superiority over all comparative benchmarks, achieving an impressive 70% accuracy, a performance on par with average human test scores.

InsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem?

TL;DR

This work tackles the sentence insertion problem by reformulating SI as a directed sentence graph and introducing InsertGNN, a global-local fused Graph Neural Network designed to capture both cross-sentence coherence and local cueing. The model combines a Global Graph Attention Network for global context with a Local Graph Network that focuses on nearby sentence interactions, and fuses these signals to predict insertion points with a triple BCE loss. On TOEFL data, InsertGNN achieves up to about 71.5% accuracy, rivaling average human performance, and demonstrates cross-domain transfer to arXiv with competitive TOEFL results, outperforming several baselines and some LLM prompts. The results validate the graph-based SI paradigm and indicate strong potential for cross-domain generalization, while also highlighting dataset limitations and avenues for future work with larger datasets and more capable LLMs.

Abstract

The integration of sentences poses an intriguing challenge within the realm of NLP, but it has not garnered the attention it deserves. Existing methods that focus on sentence arrangement, textual consistency, and question answering are inadequate in addressing this issue. To bridge this gap, we introduce InsertGNN, which conceptualizes the problem as a graph and employs a hierarchical Graph Neural Network (GNN) to comprehend the interplay between sentences. Our approach was rigorously evaluated on a TOEFL dataset, and its efficacy was further validated on the expansive arXiv dataset using cross-domain learning. Thorough experimentation unequivocally establishes InsertGNN's superiority over all comparative benchmarks, achieving an impressive 70% accuracy, a performance on par with average human test scores.

Paper Structure

This paper contains 28 sections, 4 equations, 3 figures, 6 tables.

Table of Contents

  1. Introduction and Related Work
  2. Methodology
  3. Preliminary
  4. Global Graph Attention Networks
  5. Local Graph Convolutional Networks
  6. Global-local Fusion
  7. Experiment
  8. Configurations and Datasets
  9. Setups.
  10. TOEFL exams.
  11. Baselines
  12. Results
  13. TOEFL.
  14. From ArXiv to TOEFL.
  15. Discussion
  16. ...and 13 more sections

Figures (3)

  • Figure 1: The architecture of our proposed InsertGNN.
  • Figure 2: The illustration of LGN layers..
  • Figure 3: Three graph construction algorithms. PAV only allows edges between a sentence and its preceding sentence. SSV allows for edges between a sentence and any other sentence. MSV allows multiple edges.