Papers › Semi-supervised Multitask Learning for Sequence Labeling
Semi-supervised Multitask Learning for Sequence Labeling
Marek Rei
We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset. This language modeling objective incentivises the system to learn general-purpose patterns of semantic and syntactic composition, which are also useful for improving accuracy on different sequence labeling tasks. The architecture was evaluated on a range of datasets, covering the tasks of error detection in learner texts, named entity recognition, chunking and POS-tagging. The novel language modeling objective provided consistent performance improvements on every benchmark, without requiring any additional annotated or unannotated data.
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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 |
|---|---|---|---|---|---|---|---|
| Grammatical Error Detection | CoNLL-2014 A1 | Bi-LSTM + LMcost (trained on FCE) | F0.5 | 17.86 | #6 of 8 | Archive leaderboard | report |
| Grammatical Error Detection | CoNLL-2014 A2 | Bi-LSTM + LMcost (trained on FCE) | F0.5 | 25.88 | #7 of 8 | Archive leaderboard | report |
| Grammatical Error Detection | FCE | Bi-LSTM + LMcost | F0.5 | 48.48 | #4 of 8 | Archive leaderboard | report |
| Part-Of-Speech Tagging | Penn Treebank | Bi-LSTM + LMcost | Accuracy | 97.43 | #15 of 20 | 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.
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