Awesome-Weak-Supervision-Sequence-Labeling (Awesome-WSSL)

A curated list of awesome Weak-Supervision-Sequence-Labeling (WSSL) papers, methods & resources.
Inspired by awesome-self-supervised-learning, awesome-weak-supervision, awesome-semi-supervised-learning.

• Note that: Please help contribute this list by contacting me or add pull request.

• Note that: We aim to concentrate solely on the resources within this repository that exhibit the highest relevance to Weak-Supervision-Sequence-Labeling (WSSL).

• Contents

  • 1 Surveys and Benchmarks
    • 1.1 Surveys
    • 1.2 Benchmarks
  • 2 Papers
    • 2.1 Schematic of Categorization
    • 2.2 List of Papers
      • 2.2.A Probabilistic Graphical Model
      • 2.2.B Deep Learning Model
      • 2.2.C Neural Graphical Model
  • 3 Personal Recommendation
  • 4 Other Resources

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1. Surveys and Benchmarks

1.1 Surveys

Year	Venue	Title	Implementation
2019	N/A	Sequence Labeling with Multiple Noisy Annotators	-
2021	NeurIPS	WRENCH: A Comprehensive Benchmark for Weak Supervision	Official
2022	ArXiv	A Survey on Programmatic Weak Supervision	-

1.2 Benchmarks

Year	Venue	Title	Implementation
2021	NeurIPS	WRENCH: A Comprehensive Benchmark for Weak Supervision	Official
2022	NAACL	WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding	Official

2. Papers

2.1 Schematic of Categorization

• TBD. Please stay tuned.

2.2 List of Papers

2.2.A Probabilistic Graphical Model

Year	Venue	Title	Implementation
2012	AAAI	Sembler: Ensembling Crowd Sequential Labeling for Improved Quality	-
2014	MLJ	Sequence labeling with multiple annotators	Official
2017	ACL	Aggregating and predicting sequence labels from crowd annotations	Official
2019	EMNLP	A Bayesian Approach for Sequence Tagging with Crowds	Official
2020	ACL	Named Entity Recognition without Labelled Data: A Weak Supervision Approach	Official
2020	AAAI	Weakly Supervised Sequence Tagging from Noisy Rules	Official
2021	ACL	skweak: Weak Supevision Made Easy for NLP	Official

2.2.B Deep Learning Model

Year	Venue	Title	Implementation
2017	ACL	Aggregating and predicting sequence labels from crowd annotations	Official
2018	AAAI	Deep learning from crowds	Official
2020	ACL	Learning to contextually aggregate multi-source supervision for sequence labeling	Official
2020	EMNLP Findings	OptSLA: an Optimization-Based Approach for Sequential Label Aggregation	Official
2021	ICDM	Truth Discovery in Sequence Labels from Crowds	Official
2021	ACL	Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition	Official
2023	ICDE	Learning from Noisy Crowd Labels with Logics	Official

2.3.C Neural Graphical Model

Year	Venue	Title	Implementation
2021	ACL	BERTifyingthe Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition	Official
2022	KDD	Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition	Official
2023	KDD	Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler	Official

3 Personal Recommendation

• Recent great weak-supervision benchmark called Wrench (NeuIPS-2021): WRENCH: A Comprehensive Benchmark for Weak Supervision (Code)

4 Other Resources

• Awesome-Weak-Supervision: https://github.com/JieyuZ2/Awesome-Weak-Supervision
• Awesome-Learning-with-Label-Noise: https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise
• Awesome-Noisy-Labels: https://github.com/songhwanjun/Awesome-Noisy-Labels
• A survey on crowdsourcing learning: Knowledge Learning with Crowdsourcing: A Brief Review and Systematic Perspective