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.

翻译：通过生活经验获得的微妙人文价值观支配着我们的思维，并反映在我们的言语中。它在捕捉个体本质方面起着不可或缺的作用，因而在模拟人类行为的计算系统中识别此类价值观至关重要。计算论证学是研究人类论证能力的领域，而识别此类价值观将使其受益。受此启发，我们提出了一种从论点文本中检测人类价值观的集成方法。该集成包含三个模型：（i）基于文本蕴含的模型，通过描述确定人类价值观；（ii）基于 Roberta 的分类器，从论点中预测完整的人类价值观集合；（iii）基于 Roberta 的分类器，从论点中预测简化的人类价值观集合。我们探索了多种模型组合方式并报告实验结果。此外，最佳组合在主测试集上实现了 0.48 的总体 F1 分数。