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Sentence insertion is an interesting NLP problem but received insufficientattention. Existing approaches in sentence ordering, text coherence, andquestion answering are neither suitable nor good enough at solving it. Tobridge this gap, we propose InsertGNN, a simple yet effective model thatrepresents the problem as a graph and adopts a hierarchical graph neuralnetwork (GNN) to learn the connection between sentences. We evaluate our methodin our newly collected TOEFL dataset and further verify its effectiveness onthe larger arXiv dataset using cross-domain learning. Extensive experimentsdemonstrate that InsertGNN outperforms all baselines by a large margin with anaccuracy of 70\%, rivaling the average human test scores.

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