# A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification

## TL;DR

This paper introduces a probabilistic model for yes/no crowdsourcing queries in multi-class classification, enabling effective label estimation with shorter, less demanding questions, and demonstrates its effectiveness on real datasets.

## Contribution

The work presents a novel probabilistic framework for yes/no queries in crowdsourcing, including an approximate inference method and validation on real-world scenarios.

## Key findings

- Model achieves comparable accuracy to full query methods.
- Model effectively estimates true classes by accounting for labeler failures.
- Provides publicly available datasets and code for further research.

## Abstract

Crowdsourcing has become widely used in supervised scenarios where training sets are scarce and difficult to obtain. Most crowdsourcing models in the literature assume labelers can provide answers to full questions. In classification contexts, full questions require a labeler to discern among all possible classes. Unfortunately, discernment is not always easy in realistic scenarios. Labelers may not be experts in differentiating all classes. In this work, we provide a full probabilistic model for a shorter type of queries. Our shorter queries only require "yes" or "no" responses. Our model estimates a joint posterior distribution of matrices related to labelers' confusions and the posterior probability of the class of every object. We developed an approximate inference approach, using Monte Carlo Sampling and Black Box Variational Inference, which provides the derivation of the necessary gradients. We built two realistic crowdsourcing scenarios to test our model. The first scenario queries for irregular astronomical time-series. The second scenario relies on the image classification of animals. We achieved results that are comparable with those of full query crowdsourcing. Furthermore, we show that modeling labelers' failures plays an important role in estimating true classes. Finally, we provide the community with two real datasets obtained from our crowdsourcing experiments. All our code is publicly available.