# Mixture of Experts based Multi-task Supervise Learning from Crowds

arXiv: https://arxiv.org/abs/2407.13268 · HTML: https://mlsanity.com/paper/2407.13268/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in MoE theory and gating functions, one of 12 directions in Mixture-of-experts.
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Placed in

- Mixture-of-experts / [MoE theory and gating functions](https://mlsanity.com/moe/moe-theory-and-gating-functions/)

## Builds on (most cited first, 12 of 12)

- [Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion](https://mlsanity.com/paper/1902.03680/) (2019)
- [Deep learning from crowds](https://mlsanity.com/paper/1709.01779/) (2017)
- [Learning from Crowds by Modeling Common Confusions](https://mlsanity.com/paper/2012.13052/) (2020)
- [Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds](https://mlsanity.com/paper/1905.13436/) (2019)
- [Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing](https://mlsanity.com/paper/1411.4086/) (2014)
- [Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment Classification](https://mlsanity.com/paper/1803.02781/) (2018)
- [Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization](https://mlsanity.com/paper/2306.03288/) (2023)
- [Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion](https://mlsanity.com/paper/2207.11327/) (2022)
- [Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model](https://mlsanity.com/paper/1802.04551/) (2018)
- [Crowdsourcing Semantic Label Propagation in Relation Classification](https://mlsanity.com/paper/1809.00537/) (2018)
- [Improve Learning from Crowds via Generative Augmentation](https://mlsanity.com/paper/2107.10449/) (2021)
- [Crowdsourcing Utilizing Subgroup Structure of Latent Factor Modeling](https://mlsanity.com/paper/2302.02304/) (2023)

## Built on by (most cited first, 1 of 1)

- [Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLP](https://mlsanity.com/paper/2601.09065/) (2026)

## Abstract (arXiv)

Existing truth inference methods in crowdsourcing aim to map redundant labels and items to the ground truth. They treat the ground truth as hidden variables and use statistical or deep learning-based worker behavior models to infer the ground truth. However, worker behavior models that rely on ground truth hidden variables overlook workers' behavior at the item feature level, leading to imprecise characterizations and negatively impacting the quality of truth inference. This paper proposes a new paradigm of multi-task supervised learning from crowds, which eliminates the need for modeling of items's ground truth in worker behavior models. Within this paradigm, we propose a worker behavior model at the item feature level called Mixture of Experts based Multi-task Supervised Learning from Crowds (MMLC). Two truth inference strategies are proposed within MMLC. The first strategy, named MMLC-owf, utilizes clustering methods in the worker spectral space to identify the projection vector of the oracle worker. Subsequently, the labels generated based on this vector are considered as the inferred truth. The second strategy, called MMLC-df, employs the MMLC model to fill the crowdsourced data, which can enhance the effectiveness of existing truth inference methods. Experimental results demonstrate that MMLC-owf outperforms state-of-the-art methods and MMLC-df enhances the quality of existing truth inference methods.