GLUECons: A Generic Benchmark for Learning Under Constraints

TL;DR

GLUECons introduces a comprehensive benchmark with nine tasks across NLP and computer vision, focusing on integrating external knowledge as constraints to evaluate and compare different methods systematically.

Contribution

It provides the first standardized benchmark for evaluating knowledge integration methods using constraints across multiple domains.

Findings

Models using constraints show improved interpretability.

Benchmark reveals strengths and weaknesses of different constraint integration techniques.

Extended evaluation criteria enable deeper analysis of model performance.

Abstract

Recent research has shown that integrating domain knowledge into deep learning architectures is effective -- it helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models. However, the research community is missing a convened benchmark for systematically evaluating knowledge integration methods. In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. In all cases, we model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints. We report the results of these models using a new set of extended evaluation criteria in addition to the task performances for a more in-depth analysis. This effort provides a framework for a more comprehensive and systematic comparison of constraint integration techniques and for identifying related research challenges. It will facilitate further research for alleviating some problems of state-of-the-art neural models.