Papers › Message Passing Attention Networks for Document Understanding
Message Passing Attention Networks for Document Understanding
Giannis Nikolentzos, Antoine J. -P. Tixier, Michalis Vazirgiannis
Graph neural networks have recently emerged as a very effective framework for processing graph-structured data. These models have achieved state-of-the-art performance in many tasks. Most graph neural networks can be described in terms of message passing, vertex update, and readout functions. In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD). We also propose several hierarchical variants of MPAD. Experiments conducted on 10 standard text classification datasets show that our architectures are competitive with the state-of-the-art. Ablation studies reveal further insights about the impact of the different components on performance. Code is publicly available at: https://github.com/giannisnik/mpad .
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Document Classification | BBCSport | MPAD-path | Accuracy | 99.59 | #1 of 4 | Archive leaderboard | report |
| Document Classification | MPQA | MPAD-path | Accuracy | 89.81 | #1 of 1 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | MPAD-path | Accuracy | 87.75 | #70 of 87 | Archive leaderboard | report |
| Sentiment Analysis | SST-5 Fine-grained classification | MPAD-path | Accuracy | 49.68 | #19 of 31 | Archive leaderboard | report |
| Text Classification | TREC-6 | MPAD-path | Error | 6.2 | #12 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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