Representation Learning from Limited Educational Data with Crowdsourced Labels

TL;DR

This paper introduces a novel framework for effective representation learning from limited, noisy, and crowdsourced labels, specifically tailored for educational data, using a grouping neural network, Bayesian confidence estimation, and hard example selection.

Contribution

It proposes a new deep learning framework that handles noisy crowdsourced labels and limited data, improving representation learning in educational contexts.

Findings

Outperforms state-of-the-art baselines on three real-world datasets.

Effectively captures label inconsistency with Bayesian confidence estimator.

Enhances training efficiency through adaptive hard example selection.

Abstract

Representation learning has been proven to play an important role in the unprecedented success of machine learning models in numerous tasks, such as machine translation, face recognition and recommendation. The majority of existing representation learning approaches often require a large number of consistent and noise-free labels. However, due to various reasons such as budget constraints and privacy concerns, labels are very limited in many real-world scenarios. Directly applying standard representation learning approaches on small labeled data sets will easily run into over-fitting problems and lead to sub-optimal solutions. Even worse, in some domains such as education, the limited labels are usually annotated by multiple workers with diverse expertise, which yields noises and inconsistency in such crowdsourcing settings. In this paper, we propose a novel framework which aims to learn effective representations from limited data with crowdsourced labels. Specifically, we design a grouping based deep neural network to learn embeddings from a limited number of training samples and present a Bayesian confidence estimator to capture the inconsistency among crowdsourced labels. Furthermore, to expedite the training process, we develop a hard example selection procedure to adaptively pick up training examples that are misclassified by the model. Extensive experiments conducted on three real-world data sets demonstrate the superiority of our framework on learning representations from limited data with crowdsourced labels, comparing with various state-of-the-art baselines. In addition, we provide a comprehensive analysis on each of the main components of our proposed framework and also introduce the promising results it achieved in our real production to fully understand the proposed framework.