Papers › Natural Language Inference by Tree-Based Convolution and Heuristic Matching
Natural Language Inference by Tree-Based Convolution and Heuristic Matching
Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, Zhi Jin
In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction between two sentences. In our model, a tree-based convolutional neural network (TBCNN) captures sentence-level semantics; then heuristic matching layers like concatenation, element-wise product/difference combine the information in individual sentences. Experimental results show that our model outperforms existing sentence encoding-based approaches by a large margin.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | SNLI | 300D Tree-based CNN encoders | % Test Accuracy | 82.1 | #87 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 300D Tree-based CNN encoders | % Train Accuracy | 83.3 | #87 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 300D Tree-based CNN encoders | Parameters | 3.5m | #87 of 98 | Archive leaderboard | report |
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