Combining Unsupervised and Supervised Learning for Sample Efficient Continuous Language Grounding

Abstract

Natural and efficient communication with humans requires artificial agents that are able to understand the meaning of natural language. However, understanding natural language is non-trivial and requires proper grounding mechanisms to create links between words and corresponding perceptual information. Since the introduction of the “Symbol Grounding Problem” in 1990, many different grounding approaches have been proposed that either employed supervised or unsupervised learning mechanisms. The latter have the advantage that no other agent is required to learn the correct groundings, while the former are often more sample-efficient and accurate but require the support of another agent, like a human or another artificial agent. Although combining both paradigms seems natural, it has not achieved much attention. Therefore, this paper proposes a hybrid grounding framework which combines both learning paradigms so that it is able to utilize support from a tutor, if available, while it can still learn when no support is provided. Additionally, the framework has been designed to learn in a continuous and open-ended manner so that no explicit training phase is required. The proposed framework is evaluated through two different grounding scenarios and its unsupervised grounding component is compared to a state-of-the-art unsupervised Bayesian grounding framework, while the benefit of combining both paradigms is evaluated through the analysis of different feedback rates. The obtained results show that the employed unsupervised grounding mechanism outperforms the baseline in terms of accuracy, transparency, and deployability and that combining both paradigms increases both the sample-efficiency as well as the accuracy of purely unsupervised grounding, while it ensures that the framework is still able to learn the correct mappings, when no supervision is available.

Advancing systemic disease diagnosis through ophthalmic image‐based artificial intelligence

“…Unlike supervised learning, unsupervised learning does not rely on prelabeled target variables or labels for training, focusing instead on the inherent structure and relationships within the data. 52,53 These algorithms can autonomously uncover hidden patterns or groupings in the data, enabling further exploration of similarities and differences in the information. Currently, due to limited data availability, there has been scant research applying this approach to the analysis of systemic diseases in ophthalmic AI systems.…”

“…As data volumes grow, unsupervised learning could emerge as a more feasible alternative. Unlike supervised learning, unsupervised learning does not rely on prelabeled target variables or labels for training, focusing instead on the inherent structure and relationships within the data 52,53 . These algorithms can autonomously uncover hidden patterns or groupings in the data, enabling further exploration of similarities and differences in the information.…”