Cross-Domain Classification of Moral Values (2022)

Abstract: Moral values influence how we interpret and act upon the information we receive. Identifying human moral values is essential for artificially intelligent agents to co-exist with humans. Recent progress in natural language processing allows the identification of moral values in textual discourse. However, domain-specific moral rhetoric poses challenges for transferring knowledge from one domain to another. We provide the first extensive investigation on the effects of cross-domain classification of moral values from text. We compare a state-of-the-art deep learning model (BERT) in seven domains and four cross-domain settings. We show that a value classifier can generalize and transfer knowledge to novel domains, but it can introduce catastrophic forgetting. We also highlight the typical classification errors in cross-domain value classification and compare the model predictions to the annotators agreement. Our results provide insights to computer and social scientists that seek to identify moral rhetoric specific to a domain of discourse.

## introduction morality helps humans discern right from wrong. pluralist moral philosophers argue that human morality can be represented, understood, and explained by a finite number of irreducible basic elements, referred to as moral values @xcite . the difference in our preferences over moral values explains how and why we think differently. for instance, both conservatives and liberals may agree that individual welfare is important. however, a conservative, who cherishes the values of freedom and independence, may believe that taxes should be decreased to attain more individual welfare. in contrast, a liberal, who cherishes the values of community and care, may believe that taxes should be increased to obtain welfare @xcite . it is crucial to understand human morality to develop beneficial ai @xcite . to operate among humans, artificial agents must be able to comprehend and recognize the moral values that drive the differences in human behavior @xcite . the ability to understand moral rhetoric can be instrumental for, e.g., facilitating human-agent trust @xcite and engineering value-aligned socio-technical systems @xcite @xcite . there are survey instruments to estimate individual value profiles @xcite . however, reasoning about moral values is challenging for humans @xcite . further, in practical applications, e.g., to conduct meaningful conversations @xcite or to identify online trends @xcite , artificial agents should be able to understand moral rhetoric on the fly. the growing capabilities of natural language processing (nlp) enable the estimation of moral rhetoric from textual discourse @xcite @xcite . specifically, a value classifier can be used to identify the moral values underlying a piece of text on the fly. for instance, @xcite show that detecting moral values from tweets can predict violent protests. existing value classifiers are evaluated on a specific dataset, without re-training or testing the classifier on a different dataset. this shows the ability of the classifier to predict values from text, but not the ability to transfer the learned knowledge across datasets. a critical aspect of moral values is that they are intrinsically linked to the domain under discussion @xcite @xcite . moral value expressions may take different forms in different domains. for example, in the driving domain, the value of safety concerns speed limits and seat belts, but in the covid-19 domain, safety concerns social distancing and face masks. further, a word (broadly, language) may trigger different moral rhetoric in different domains. for example, in a libertarian blog, the word 'taxes' may be linked to the authority value, but in a socialist blog it may be linked to the community value. thus, it is crucial for a value classifier to recognize domain-specific connotations of moral rhetoric. collecting and annotating a sufficient amount of training examples in each domain is expensive and time consuming. to reduce the need for new annotated examples, we can pretrain classifiers with similar available annotated data and transfer the acquired knowledge to a novel task-a practice known as transfer learning @xcite . despite the benefits, transfer learning poses wellknown challenges, including: (1) generalizability: how well does a classifier perform on novel data? (2) transferability: how well is knowledge transferred from one domain to another? and (3) catastrophic forgetting: to what extent is knowledge of a previous domain lost after training in a new domain? these challenges are crucial for value classification because of its domain-specific nature. we perform the first comprehensive crossdomain evaluation of a value classifier. we employ the moral foundation twitter corpus @xcite , consisting of seven datasets spanning different socio-political areas, annotated with the value taxonomy of the moral foundation theory @xcite . treating each dataset as a domain, we train a deep learning model, bert @xcite , in four training settings to evaluate the value classifier's generalizability, transferability, and catastrophic forgetting. our experiments show that (1) a value classifier can generalize to novel domains, especially when trained on a variety of domains; (2) initializing a classifier with examples from different domains improves performance in novel domains even when little training data is available in the novel domains; our investigation is significant because moral rhetoric is seldom explicit in language, but often lies in subtle domain-dependent cues. understand-ing whether a classifier can recognize and transfer such hidden patterns across domains is instrumental for the practical use. by unveiling the successes and mistakes of value classifiers in cross-domain settings, we hope to inspire researchers and practitioners to employ value classification responsibly. ## background and data we introduce the moral foundation theory (mft) @xcite and the moral foundation twitter corpus (mftc) @xcite used in our experiments. the mft is a well-established theory of moral values developed by social and cultural psychologists. it argues that human morality is composed of a finite set of innate moral foundations, similar to how the five taste receptors (for sweet, sour, salt, bitter, and umami) combine to yield the tastes we experience. the mft includes five foundations, each composed of a vice-virtue duality, resulting in the 10 moral values shown in the tweets were annotated by multiple annotators with the mft taxonomy. @xcite provide additional details on the annotation process. they recognize that the vice and the virtue constituting one moral foundation are expressed differently in natural language. for example, an ut-terance describing a care concern (e.g., taking care of one's offspring) does not necessarily also contain harm expressions. for this reason, each tweet was annotated with all 10 individual moral values plus an additional nonmoral label, resulting in 11 possible labels per tweet. due to the subjective nature of moral values, different annotators may label the same tweet differently. for this reason, @xcite apply a majority vote to select the definitive label(s) of each tweet. tweets with no majority label are labeled as nonmoral. ## experimental setup predicting moral values is a multi-label classification problem. given a set of textual documents, t , and a set of moral value labels, @xmath0, l 2 , . . . , l n ), we wish to learn a mapping c : @xmath1. each element in p(l) is a binary vector, @xmath2, y 2 , . . . , y n ), where y @xmath3. the mapping c is learned via bert @xcite , a language representation model based on the transformer architecture @xcite . we choose bert as it represents the state-of-the-art for several nlp tasks, including value classification @xcite @xcite . we provide additional details, including hyperparameters, in the appendix. the code is available on github foot_0 . ## results and discussion we evaluate the performance of the model in four training scenarios (source, target, f inetune, all). we perform wilcoxon's ranksum test @xcite to evaluate whether two results significantly differ or not. in each column (and in the top-half or the bottom-half), we choose the setting with the highest f 1 -score and perform a pair-wise comparison with each of the other settings in that (half) column. we highlight, in bold, the best result and the results that are not significantly different (@xmath4.05) from the best. ## related work we review closely related works on value estimation from text, and on cross-domain classification in nlp subfields relevant to value classification. ## conclusions and directions we perform a comprehensive cross-domain evaluation of a multi-label value classifier, by comparing a deep learning model (bert) in seven domains with four cross-domain training scenarios. our aim is to support practical applications of moral rhetoric classification, e.g., the detection of radicalism through the study of moral homogeneity @xcite , the prediction of violent protests @xcite , the identification of moral concerns of citizens @xcite , and the extraction of moral rhetoric supporting both stances and arguments @xcite . our findings inform both computer scientists and social scientists on training value classifiers. however, we do not provide a fixed recipe since the right model and approach depend on the time, resources, and data available. we show that a value classifier generally exhibits the ability to classify moral values across domains. however, the results are highly dependent on the distribution of moral rhetoric in a domain. our experiments support the following key findings. first, a value classifier can generalize to novel domains, especially when trained on multiple domains. however, its performance on the novel domain improves even when trained with a small portion of data from the novel domain. second, pretraining a value classifier with data from different domains has three benefits when finetuning the classifier. it yields (1) better performances on the novel domain than other settings, (2) good performances even when little training data is available in the novel domain, and (3) smaller confusion among moral values, especially among those less frequent in the novel domain. third, finetuning on a novel domain causes catastrophic forgetting of the domain it was pretrained with, even when finetuning on a small portion of data from the novel domain. thus, the tradeoff between benefits of transferability and adverse effects of forgetting must be considered in choosing the extent of finetuning. finally, despite the challenging nature of cross-domain value classification, the majority of classification errors are not severe in that, in all evaluation settings, at least one annotator agrees with the model prediction. our investigation opens avenues for additional experiments with advanced methods to improve transfer learning @xcite @xcite and mitigate catastrophic forgetting @xcite @xcite . further, based on the analysis of classification errors, we suggest incorporating the annotators (dis-) agreement into the training of the model, e.g., by employing the full distributions of annotations, as opposed to the current majority approach @xcite .