Papers › Sentence-State LSTM for Text Representation
Sentence-State LSTM for Text Representation
Yue Zhang, Qi Liu, Linfeng Song
Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, which consists of a parallel state for each word. Recurrent steps are used to perform local and global information exchange between words simultaneously, rather than incremental reading of a sequence of words. Results on various classification and sequence labelling benchmarks show that the proposed model has strong representation power, giving highly competitive performances compared to stacked BiLSTM models with similar parameter numbers.
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Code Syntology ran Syntology
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7a057b727d573936 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Named Entity Recognition (NER) | CoNLL 2003 (English) | S-LSTM | F1 | 91.57 | #59 of 73 | Archive leaderboard | report |
| Part-Of-Speech Tagging | Penn Treebank | S-LSTM | Accuracy | 97.55 | #12 of 20 | Archive leaderboard | report |
| Sentiment Analysis | IMDb | S-LSTM | Accuracy | 87.15 | #44 of 49 | Archive leaderboard | report |
| Sentiment Analysis | MR | S-LSTM | Accuracy | 76.2 | #16 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.
Methods
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