2018/11/02 by Edwin Simpson, Iryna Gurevych, Simpson, Edwin +1
Computer Science · #Computation and Language (cs.CL) #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Mobile Crowdsensing and Crowdsourcing #Topic Modeling #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.1811.00780
Accepted for EMNLP 2019
openalex publication_date 2018/11/02 · arxiv created 2019/09/06 · arxiv updated 2019/09/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Current methods for sequence tagging, a core task in NLP, are data hungry, which motivates the use of crowdsourcing as a cheap way to obtain labelled data. However, annotators are often unreliable and current aggregation methods cannot capture common types of span annotation errors. To address this, we propose a Bayesian method for aggregating sequence tags that reduces errors by modelling sequential dependencies between the annotations as well as the ground-truth labels. By taking a Bayesian approach, we account for uncertainty in the model due to both annotator errors and the lack of data for modelling annotators who complete few tasks. We evaluate our model on crowdsourced data for named entity recognition, information extraction and argument mining, showing that our sequential model outperforms the previous state of the art. We also find that our approach can reduce crowdsourcing costs through more effective active learning, as it better captures uncertainty in the sequence labels when there are few annotations.