Papers with neural sequence labeling
NCRF++: An Open-source Neural Sequence Labeling Toolkit (P18-4)
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| Challenge: | Existing statistical approaches to neural sequence labeling have been used for many tasks. |
| Approach: | They describe a toolkit for neural sequence labeling that provides a CRF inference layer for quick implementation. |
| Outcome: | The toolkit is based on PyTorch and can be run on GPUs. |
Hybrid semi-Markov CRF for Neural Sequence Labeling (P18-2)
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| Challenge: | Existing conditional random fields (CRFs) use hand-crafted features to perform sequence labeling tasks. |
| Approach: | They propose to use semi-Markov conditional random fields for neural sequence labeling in natural language processing to extract features from segments instead of words. |
| Outcome: | The proposed model achieves state-of-the-art when no external knowledge is used. |
Hierarchically-Refined Label Attention Network for Sequence Labeling (D19-1)
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| Challenge: | Conditional random fields (CRF) is a powerful model for statistical sequence labeling, but it does not give much information gain over strong neural encoding. |
| Approach: | They propose a hierarchically-refined label attention network which captures potential long-term label dependency by giving each word incrementally refined label distributions with hierarchical attention. |
| Outcome: | The proposed model improves POS tagging accuracy and speeds up training and testing compared to the current model. |
Handling Rare Entities for Neural Sequence Labeling (2020.acl-main)
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| Challenge: | Recent approaches to sequence labeling have been based on statistical models but a challenge is from the data sparsity problem. |
| Approach: | They propose to use local context reconstruction to implicitly incorporate contextual information into their representations. |
| Outcome: | The proposed model outperforms all previous methods on multiple benchmark datasets and achieves new start-of-the-art results. |