Rudolf Christoph Eucken at SemEval-2023 Task 4: An Ensemble Approach for Identifying Human Values from Arguments

TL;DR

This paper presents an ensemble method combining three models to identify human values from argument texts, achieving a notable F1 score of 0.48 on SemEval-2023 Task 4.

Contribution

It introduces a novel ensemble approach integrating entailment and Roberta-based classifiers for human value detection in arguments.

Findings

Achieved an F1 score of 0.48 on the main test set.

Demonstrated the effectiveness of model combination strategies.

Provided insights into ensemble model performance for value detection.

Abstract

The subtle human values we acquire through life experiences govern our thoughts and gets reflected in our speech. It plays an integral part in capturing the essence of our individuality and making it imperative to identify such values in computational systems that mimic human actions. Computational argumentation is a field that deals with the argumentation capabilities of humans and can benefit from identifying such values. Motivated by that, we present an ensemble approach for detecting human values from argument text. Our ensemble comprises three models: (i) An entailment-based model for determining the human values based on their descriptions, (ii) A Roberta-based classifier that predicts the set of human values from an argument. (iii) A Roberta-based classifier to predict a reduced set of human values from an argument. We experiment with different ways of combining the models and report our results. Furthermore, our best combination achieves an overall F1 score of 0.48 on the main test set.