Bag-of-Words vs. Graph vs. Sequence in Text Classification: Questioning the Necessity of Text-Graphs and the Surprising Strength of a Wide MLP. (arXiv:2109.03777v3 [cs.CL] UPDATED)

Graph neural networks have triggered a resurgence of graph-based text
classification methods, defining today's state of the art. We show that a wide
multi-layer perceptron (MLP) using a Bag-of-Words (BoW) outperforms the recent
graph-based models TextGCN and HeteGCN in an inductive text classification
setting and is comparable with HyperGAT. Moreover, we fine-tune a
sequence-based BERT and a lightweight DistilBERT model, which both outperform
all state-of-the-art models. These results question the importance of synthetic
graphs used in modern text classifiers. In …

arxiv bag classification graph graphs mlp text text classification words