Papers › Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER
Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER
Peng-Hsuan Li, Tsu-Jui Fu, Wei-Yun Ma
BiLSTM has been prevalently used as a core module for NER in a sequence-labeling setup. State-of-the-art approaches use BiLSTM with additional resources such as gazetteers, language-modeling, or multi-task supervision to further improve NER. This paper instead takes a step back and focuses on analyzing problems of BiLSTM itself and how exactly self-attention can bring improvements. We formally show the limitation of (CRF-)BiLSTM in modeling cross-context patterns for each word -- the XOR limitation. Then, we show that two types of simple cross-structures -- self-attention and Cross-BiLSTM -- can effectively remedy the problem. We test the practical impacts of the deficiency on real-world NER datasets, OntoNotes 5.0 and WNUT 2017, with clear and consistent improvements over the baseline, up to 8.7% on some of the multi-token entity mentions. We give in-depth analyses of the improvements across several aspects of NER, especially the identification of multi-token mentions. This study should lay a sound foundation for future improvements on sequence-labeling NER. (Source codes: https://github.com/jacobvsdanniel/cross-ner)
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Named Entity Recognition (NER) | Ontonotes v5 (English) | Att-BiLSTM-CNN | F1 | 88.4 | #20 of 28 | Archive leaderboard | report |
| Named Entity Recognition (NER) | Ontonotes v5 (English) | Att-BiLSTM-CNN | Precision | 88.71 | #20 of 28 | Archive leaderboard | report |
| Named Entity Recognition (NER) | Ontonotes v5 (English) | Att-BiLSTM-CNN | Recall | 88.11 | #20 of 28 | Archive leaderboard | report |
| Named Entity Recognition (NER) | WNUT 2017 | Cross-BiLSTM-CNN | F1 | 42.85 | #20 of 23 | Archive leaderboard | report |
| Named Entity Recognition (NER) | WNUT 2017 | Cross-BiLSTM-CNN | Precision | 58.28 | #20 of 23 | Archive leaderboard | report |
| Named Entity Recognition (NER) | WNUT 2017 | Cross-BiLSTM-CNN | Recall | 33.92 | #20 of 23 | 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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