# Truth Discovery in Sequence Labels from Crowds. (arXiv:2109.04470v1 [cs.HC])

Annotations quality and quantity positively affect the performance of
sequence labeling, a vital task in Natural Language Processing. Hiring domain
experts to annotate a corpus set is very costly in terms of money and time.
Crowdsourcing platforms, such as Amazon Mechanical Turk (AMT), have been
deployed to assist in this purpose. However, these platforms are prone to human
errors due to the lack of expertise; hence, one worker’s annotations cannot be
directly used to train the model. Existing literature in annotation aggregation
more focuses on binary or multi-choice problems. In recent years, handling the
sequential label aggregation tasks on imbalanced datasets with complex
dependencies between tokens has been challenging. To conquer the challenge, we
propose an optimization-based method that infers the best set of aggregated
annotations using labels provided by workers. The proposed Aggregation method
for Sequential Labels from Crowds ($AggSLC$) jointly considers the
characteristics of sequential labeling tasks, workers’ reliabilities, and
advanced machine learning techniques. We evaluate $AggSLC$ on different
crowdsourced data for Named Entity Recognition (NER), Information Extraction
tasks in biomedical (PICO), and the simulated dataset. Our results show that
the proposed method outperforms the state-of-the-art aggregation methods. To
achieve insights into the framework, we study $AggSLC$ components’
effectiveness through ablation studies by evaluating our model in the absence
of the prediction module and inconsistency loss function. Theoretical analysis
of our algorithm’s convergence points that the proposed $AggSLC$ halts after a
finite number of iterations.