Papers › Neural Tree Indexers for Text Understanding
Neural Tree Indexers for Text Understanding
Tsendsuren Munkhdalai, Hong Yu
Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce a robust syntactic parsing-independent tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic treebased recursive models. NTI constructs a full n-ary tree by processing the input text with its node function in a bottom-up fashion. Attention mechanism can then be applied to both structure and node function. We implemented and evaluated a binarytree model of NTI, showing the model achieved the state-of-the-art performance on three different NLP tasks: natural language inference, answer sentence selection, and sentence classification, outperforming state-of-the-art recurrent and recursive neural networks.
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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 |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | SNLI | 300D Full tree matching NTI-SLSTM-LSTM w/ global attention | % Test Accuracy | 87.3 | #46 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 300D Full tree matching NTI-SLSTM-LSTM w/ global attention | % Train Accuracy | 88.5 | #46 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 300D Full tree matching NTI-SLSTM-LSTM w/ global attention | Parameters | 3.2m | #46 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 300D NTI-SLSTM-LSTM encoders | % Test Accuracy | 83.4 | #84 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 300D NTI-SLSTM-LSTM encoders | % Train Accuracy | 82.5 | #84 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 300D NTI-SLSTM-LSTM encoders | Parameters | 4.0m | #84 of 98 | 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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