Papers › On Tree-Based Neural Sentence Modeling
On Tree-Based Neural Sentence Modeling
Haoyue Shi, Hao Zhou, Jiaze Chen, Lei LI
Neural networks with tree-based sentence encoders have shown better results on many downstream tasks. Most of existing tree-based encoders adopt syntactic parsing trees as the explicit structure prior. To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i.e., binary balanced tree, left-branching tree and right-branching tree) in the encoders. Though trivial trees contain no syntactic information, those encoders get competitive or even better results on all of the ten downstream tasks we investigated. This surprising result indicates that explicit syntax guidance may not be the main contributor to the superior performances of tree-based neural sentence modeling. Further analysis show that tree modeling gives better results when crucial words are closer to the final representation. Additional experiments give more clues on how to design an effective tree-based encoder. Our code is open-source and available at https://github.com/ExplorerFreda/TreeEnc.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | Amazon Review Full | Gumbel+bi-leaf-RNN | Accuracy | 49.7 | #9 of 9 | Archive leaderboard | report |
| Sentiment Analysis | Amazon Review Polarity | Gumbel+bi-leaf-RNN | Accuracy | 88.1 | #9 of 9 | Archive leaderboard | report |
| Text Classification | AG News | Balanced+bi-leaf-RNN | Error | 7.9 | #16 of 24 | Archive leaderboard | report |
| Text Classification | DBpedia | Balanced+bi-leaf-RNN | Error | 1.2 | #15 of 21 | 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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