  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.
