Meta-learning Representations for Learning from Multiple Annotators

TL;DR

This paper introduces a meta-learning approach that leverages related tasks and latent space embeddings to effectively learn from multiple noisy annotators, especially when limited annotated data is available.

Contribution

The proposed method combines meta-learning, latent space embedding, and EM algorithm to improve learning from noisy annotations with scarce data, outperforming existing approaches.

Findings

Effective in real-world noisy datasets

Improves classification accuracy with limited data

Efficient backpropagation through EM steps

Abstract

We propose a meta-learning method for learning from multiple noisy annotators. In many applications such as crowdsourcing services, labels for supervised learning are given by multiple annotators. Since the annotators have different skills or biases, given labels can be noisy. To learn accurate classifiers, existing methods require many noisy annotated data. However, sufficient data might be unavailable in practice. To overcome the lack of data, the proposed method uses labeled data obtained in different but related tasks. The proposed method embeds each example in tasks to a latent space by using a neural network and constructs a probabilistic model for learning a task-specific classifier while estimating annotators' abilities on the latent space. This neural network is meta-learned to improve the expected test classification performance when the classifier is adapted to a given small amount of annotated data. This classifier adaptation is performed by maximizing the posterior probability via the expectation-maximization (EM) algorithm. Since each step in the EM algorithm is easily computed as a closed-form and is differentiable, the proposed method can efficiently backpropagate the loss through the EM algorithm to meta-learn the neural network. We show the effectiveness of our method with real-world datasets with synthetic noise and real-world crowdsourcing datasets.