Papers › Hierarchically-Refined Label Attention Network for Sequence Labeling
Hierarchically-Refined Label Attention Network for Sequence Labeling
Leyang Cui, Yue Zhang
CRF has been used as a powerful model for statistical sequence labeling. For neural sequence labeling, however, BiLSTM-CRF does not always lead to better results compared with BiLSTM-softmax local classification. This can be because the simple Markov label transition model of CRF does not give much information gain over strong neural encoding. For better representing label sequences, we investigate a hierarchically-refined label attention network, which explicitly leverages label embeddings and captures potential long-term label dependency by giving each word incrementally refined label distributions with hierarchical attention. Results on POS tagging, NER and CCG supertagging show that the proposed model not only improves the overall tagging accuracy with similar number of parameters, but also significantly speeds up the training and testing compared to BiLSTM-CRF.
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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 |
|---|---|---|---|---|---|---|---|
| CCG Supertagging | CCGbank | BiLSTM-LAN | Accuracy | 94.7 | #5 of 8 | Archive leaderboard | report |
| Named Entity Recognition (NER) | Ontonotes v5 (English) | BiLSTM-LAN | F1 | 88.16 | #21 of 28 | Archive leaderboard | report |
| Part-Of-Speech Tagging | Penn Treebank | BiLSTM-LAN | Accuracy | 97.65 | #7 of 20 | Archive leaderboard | report |
| Part-Of-Speech Tagging | UD | BiLSTM-LAN | Avg accuracy | 96.88 | #1 of 5 | 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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