mlsanityData to 7 Oct 2026

Semi-supervised Multitask Learning for Sequence Labeling

arXiv 1704.07156 ↗First posted 24 Apr 2017cs.CL cs.LG cs.NEcited by 20

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Abstract (arXiv)

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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Apr 2017.

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Citing papers per year: 2021: 8; 2022: 5; 2023: 4; 2024: 2; 2025: 1.

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  1. 2021A Survey on Deep Semi-supervised Learningcited by 250
  2. 2021BERT: A Review of Applications in Natural Language Processing and Understandingcited by 109
  3. 2021Multi-Task Learning in Natural Language Processing: An Overviewcited by 61
  4. 2022A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methodscited by 42
  5. 2021Structural Information Preserving for Graph-to-Text Generationcited by 24
  6. 2021Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correctioncited by 11
  7. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  8. 2021Recent Trends in Named Entity Recognition (NER)cited by 9
  9. 2025Ensemble Learning for Large Language Models in Text and Code Generation: A Surveycited by 8
  10. 2023Pay More Attention to Relation Exploration for Knowledge Base Question Answeringcited by 7
  11. 2023Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategiescited by 6
  12. 2023Assessing Phrase Break of ESL Speech with Pre-trained Language Models and Large Language Modelscited by 5
8 more
  1. 2021PCA-based Multi Task Learning: a Random Matrix Approachcited by 4
  2. 2021Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errorscited by 2
  3. 2022In Situ Answer Sentence Selection at Web-scalecited by 2
  4. 2022Learning Multitask Gaussian Bayesian Networkscited by 1
  5. 2022Assessing Phrase Break of ESL speech with Pre-trained Language Modelscited by 0
  6. 2023Dynamic Named Entity Recognitioncited by 0
  7. 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
  8. 2024Enhancing Grammatical Error Detection using BERT with Cleaned Lang-8 Datasetcited by 0