Lauri Ingman at S em E val-2023 Task 4: A Chain Classifier for Identifying Human Values behind Arguments (2023)

Abstract: Identifying expressions of human values in textual data is a crucial albeit complicated challenge, not least because ethics are highly variable, often implicit, and transcend circumstance. Opinions, arguments, and the like are generally founded upon more than one guiding principle, which are not necessarily independent. As such, little is known about how to classify and predict moral undertones in natural language sequences. Here, we describe and present a solution to ValueEval, our shared contribution to SemEval 2023 Task 4. Our research design focuses on investigating chain classifier architectures with pretrained contextualized embeddings to detect 20 different human values in written arguments. We show that our best model substantially surpasses the classification performance of the baseline method established in prior work. We discuss limitations to our approach and outline promising directions for future work.

## introduction 1.motivation human values are inextricably linked to the formation of thoughts, opinions, and actions. they underlie an individual's moral, ethical, and philosophical convictions which are manifest in their worldview. in essence, public and private discourse are expressions of human values. as online communication platforms increasingly provide social alternatives to the "town square," understanding written content, as it relates to the human values contained within it, is an interesting and important nlp challenge. valueeval presents a unique opportunity to investigate the human values behind the formation and presentation of opinions on controversial topics, particularly those with social, economic, and political implications @xcite . the main objective of valueeval 1 is to detect 1 https://touche.webis.de/semeval23/ touche23-web/index.html#evaluation human values in arguments. given an argument with three attributes and a finite set of human value categories, the system must classify whether or not the argument is associated with a given value for all values in the set. valueeval is composed of a single task rather than sub-tasks; however, the committee specifies the option to focus on a subset of the 20 human value categories for classification. here, we employ a classification model using all 20 human value categories. ## results we reference the original task paper to establish a baseline @xcite . the authors separate tasks into different levels of granularity. we focus on level 1, the highest granularity. @xcite test three level 1 baseline models: bert, an svm, and a simple model that always outputs 1. the highest f1-score in this context is 0.25, with bert. our model achieves an f1-score of 0.67, a significant improvement from the baseline. we compare our results with the f1 evaluation metric in