Papers › Semi-supervised Multitask Learning for Sequence Labeling

Semi-supervised Multitask Learning for Sequence Labeling

24 Apr 2017ACL 2017 7arXiv:1704.07156archive 2025-07-28

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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Tasks

ChunkingGrammatical Error DetectionLanguage ModelingLanguage ModellingNamed Entity RecognitionNamed Entity Recognition (NER)POSPOS TaggingPart-Of-Speech Tagging

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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