Bag-of-Words vs. Graph vs. Sequence in Text Classification: Questioning the Necessity of Text-Graphs and the Surprising Strength of a Wide MLP
Lukas Paul Achatius Galke, Ansgar Scherp · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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 Hete-GCN 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 terms of efficiency, DistilBERT is still twice as large as our BoW-based wide MLP, while graph-based models like TextGCN require setting up an O(N 2 ) graph, where N is the vocabulary plus corpus size.Finally, since Transformers need to compute O(L 2 ) attention weights with sequence length L, the MLP models show higher training and inference speeds on datasets with long sequences. Model Synthetic Graph Position-Aware Arbitrary Length InductiveBag-of-Words No No Yes Yes Graph: TextGCN Yes No Yes No Graph: