TLLC: Transfer Learning-based Label Completion for Crowdsourcing

Wenjun Zhang, Liangxiao Jiang, Chaoqun Li
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:75178-75191, 2025.

Abstract

Label completion serves as a preprocessing approach to handling the sparse crowdsourced label matrix problem, significantly boosting the effectiveness of the downstream label aggregation. In recent advances, worker modeling has been proved to be a powerful strategy to further improve the performance of label completion. However, in real-world scenarios, workers typically annotate only a few instances, leading to insufficient worker modeling and thus limiting the improvement of label completion. To address this issue, we propose a novel transfer learning-based label completion (TLLC) method. Specifically, we first identify all high-confidence instances from the whole crowdsourced data as a source domain and use it to pretrain a Siamese network. The abundant annotated instances in the source domain provide essential knowledge for worker modeling. Then, we transfer the pretrained network to the target domain with the instances annotated by each worker separately, ensuring worker modeling captures unique characteristics of each worker. Finally, we leverage the new embeddings learned by the transferred network to complete each worker’s missing labels. Extensive experiments on several widely used real-world datasets demonstrate the effectiveness of TLLC. Our codes and datasets are available at https://github.com/jiangliangxiao/TLLC.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-zhang25ah, title = {{TLLC}: Transfer Learning-based Label Completion for Crowdsourcing}, author = {Zhang, Wenjun and Jiang, Liangxiao and Li, Chaoqun}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {75178--75191}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/zhang25ah/zhang25ah.pdf}, url = {https://proceedings.mlr.press/v267/zhang25ah.html}, abstract = {Label completion serves as a preprocessing approach to handling the sparse crowdsourced label matrix problem, significantly boosting the effectiveness of the downstream label aggregation. In recent advances, worker modeling has been proved to be a powerful strategy to further improve the performance of label completion. However, in real-world scenarios, workers typically annotate only a few instances, leading to insufficient worker modeling and thus limiting the improvement of label completion. To address this issue, we propose a novel transfer learning-based label completion (TLLC) method. Specifically, we first identify all high-confidence instances from the whole crowdsourced data as a source domain and use it to pretrain a Siamese network. The abundant annotated instances in the source domain provide essential knowledge for worker modeling. Then, we transfer the pretrained network to the target domain with the instances annotated by each worker separately, ensuring worker modeling captures unique characteristics of each worker. Finally, we leverage the new embeddings learned by the transferred network to complete each worker’s missing labels. Extensive experiments on several widely used real-world datasets demonstrate the effectiveness of TLLC. Our codes and datasets are available at https://github.com/jiangliangxiao/TLLC.} }
Endnote
%0 Conference Paper %T TLLC: Transfer Learning-based Label Completion for Crowdsourcing %A Wenjun Zhang %A Liangxiao Jiang %A Chaoqun Li %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-zhang25ah %I PMLR %P 75178--75191 %U https://proceedings.mlr.press/v267/zhang25ah.html %V 267 %X Label completion serves as a preprocessing approach to handling the sparse crowdsourced label matrix problem, significantly boosting the effectiveness of the downstream label aggregation. In recent advances, worker modeling has been proved to be a powerful strategy to further improve the performance of label completion. However, in real-world scenarios, workers typically annotate only a few instances, leading to insufficient worker modeling and thus limiting the improvement of label completion. To address this issue, we propose a novel transfer learning-based label completion (TLLC) method. Specifically, we first identify all high-confidence instances from the whole crowdsourced data as a source domain and use it to pretrain a Siamese network. The abundant annotated instances in the source domain provide essential knowledge for worker modeling. Then, we transfer the pretrained network to the target domain with the instances annotated by each worker separately, ensuring worker modeling captures unique characteristics of each worker. Finally, we leverage the new embeddings learned by the transferred network to complete each worker’s missing labels. Extensive experiments on several widely used real-world datasets demonstrate the effectiveness of TLLC. Our codes and datasets are available at https://github.com/jiangliangxiao/TLLC.
APA
Zhang, W., Jiang, L. & Li, C.. (2025). TLLC: Transfer Learning-based Label Completion for Crowdsourcing. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:75178-75191 Available from https://proceedings.mlr.press/v267/zhang25ah.html.

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