Papers › Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Kai Sheng Tai, Richard Socher, Christopher D. Manning
Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks. The only underlying LSTM structure that has been explored so far is a linear chain. However, natural language exhibits syntactic properties that would naturally combine words to phrases. We introduce the Tree-LSTM, a generalization of LSTMs to tree-structured network topologies. Tree-LSTMs outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task 1) and sentiment classification (Stanford Sentiment Treebank).
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Code
Syntology Ran 6 of 15 code samples harvested from 5 repositories linked to this paper; 9 have no recorded run. Of those that ran: 4 ran · our draft was wrong; 2 ran · fixture could not drive it.
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16 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
15 samples harvested; 6 ran; 0 honoured the contract we drafted; 9 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 6 of the 15 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Similarity | SICK | Dependency Tree-LSTM (Tai et al., 2015) | MSE | 0.2532 | #1 of 5 | Archive leaderboard | report |
| Semantic Similarity | SICK | Dependency Tree-LSTM (Tai et al., 2015) | Pearson Correlation | 0.8676 | #1 of 5 | Archive leaderboard | report |
| Semantic Similarity | SICK | Dependency Tree-LSTM (Tai et al., 2015) | Spearman Correlation | 0.8083 | #1 of 5 | Archive leaderboard | report |
| Semantic Similarity | SICK | Bidirectional LSTM (Tai et al., 2015) | MSE | 0.2736 | #3 of 5 | Archive leaderboard | report |
| Semantic Similarity | SICK | Bidirectional LSTM (Tai et al., 2015) | Pearson Correlation | 0.8567 | #3 of 5 | Archive leaderboard | report |
| Semantic Similarity | SICK | Bidirectional LSTM (Tai et al., 2015) | Spearman Correlation | 0.7966 | #3 of 5 | Archive leaderboard | report |
| Semantic Similarity | SICK | LSTM (Tai et al., 2015) | MSE | 0.2831 | #4 of 5 | Archive leaderboard | report |
| Semantic Similarity | SICK | LSTM (Tai et al., 2015) | Pearson Correlation | 0.8528 | #4 of 5 | Archive leaderboard | report |
| Semantic Similarity | SICK | LSTM (Tai et al., 2015) | Spearman Correlation | 0.7911 | #4 of 5 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | Consistency Tree LSTM with tuned Glove vectors [tai2015improved] | Accuracy | 88.0 | #68 of 87 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | 2-layer LSTM [tai2015improved] | Accuracy | 86.3 | #77 of 87 | Archive leaderboard | report |
| Sentiment Analysis | SST-5 Fine-grained classification | Constituency Tree-LSTM | Accuracy | 51.0 | #17 of 31 | 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.
Methods
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