AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network (2020.emnlp-main)
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| Challenge: | Existing approaches to sequence labeling require sequential computation that makes parallelization impossible. |
| Approach: | They propose to employ a parallelizable approximate variational inference algorithm for the CRF model. |
| Outcome: | The proposed approach improves decoding speed and accuracy with long sentences and is parallelizable for faster training and prediction. |
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| Challenge: | Existing methods for sequence labeling are hidden Markov models and conditional random fields (CRF). |
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Uncertainty-Aware Label Refinement for Sequence Labeling (2020.emnlp-main)
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| Challenge: | Conditional random fields (CRF) for label decoding have been a problem for many tasks. |
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Masked Conditional Random Fields for Sequence Labeling (2021.naacl-main)
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| Challenge: | Conditional Random Fields (CRF) based neural models are among the most performant for sequence labeling problems, but they can sometimes generate illegal sequences of tags. |
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Hybrid semi-Markov CRF for Neural Sequence Labeling (P18-2)
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| Challenge: | Existing approaches to sequence labeling are based on the neural linear-chain CRF model. |
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Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors (2020.emnlp-main)
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| Challenge: | Recent advances in NLP focus on simple approaches to model the output label space . graphical models are often limited to (heuristic) greedy search and its variants . |
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Filtered Semi-Markov CRF (2023.findings-emnlp)
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| Challenge: | Existing methods for sequence labeling tasks such as Named Entity Recognition (NER) suffer from quadratic complexity over sequence length and poor performance compared to CRF. |
| Approach: | They propose a variant of Semi-Markov CRF that incorporates a filtering step to eliminate irrelevant segments, reducing complexity and search space. |
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An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks (2020.emnlp-main)
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| Challenge: | Recent work shows that conditional random fields (CRFs) perform well in sequence labeling tasks. |
| Approach: | They propose several high-order energy terms to capture dependencies among labels in sequence labeling . they use convolutional, recurrent, and self-attention networks to construct these energy terms . |
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