The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments (2022)

Abstract: An audience’s prior beliefs and morals are strong indicators of how likely they will be affected by a given argument. Utilizing such knowledge can help focus on shared values to bring disagreeing parties towards agreement. In argumentation technology, however, this is barely exploited so far. This paper studies the feasibility of automatically generating morally framed arguments as well as their effect on different audiences. Following the moral foundation theory, we propose a system that effectively generates arguments focusing on different morals. In an in-depth user study, we ask liberals and conservatives to evaluate the impact of these arguments. Our results suggest that, particularly when prior beliefs are challenged, an audience becomes more affected by morally framed arguments.

## introduction in the last years, more research has been dedicated to studying prior beliefs in argumentation. understanding the role of prior beliefs helps people craft more effective arguments when targeting a particular audience. accordingly, operationalizing knowledge about the audience as part of supporting tools for humans or realized in fully automated debating technologies @xcite could benefit the production of arguments that bridge the gap between disputed parties by focusing on the shared beliefs rather than divisive ones @xcite . in social psychology, a body of research employs the notion of morals to understand people's judgments on controversial topics @xcite . @xcite demonstrated that arguments become more effective when they match the morals of the target audience. multiple works in computational linguistics have analyzed the persuasive effectiveness of arguments depending on the target audience @xcite , showing that audience-based features reliably predict effectiveness. different proxies of beliefs have been proposed as part of this, ranging from interests and personality traits @xcite to stances on popular issues @xcite . the authors of the latter used the stances to control the generation of argumentative texts. however, they did not assess the effectiveness of these texts on the audience, leaving the importance of encoding beliefs ultimately unclear. beyond that, little research has been done on generating arguments tailored towards a specific audience, let alone on the importance of morals in achieving agreement. this work studies the feasibility of generating morally framed arguments computationally and the effect of these arguments on different audiences. for this, we rely on the moral foundation theory @xcite that projects the moral system into five foundations: care, fairness, loyalty, authority, and purity. to produce arguments that address specific morals, we extend the capabilities of project debater @xcite . 1 project debater includes a hybrid approach of multiple components designed to generate arguments of high quality that compete with human arguments. building on this technology helps us focus on evaluating the impact of morally framed arguments, as it ensures a certain base quality level in the generation. in particular, our proposed extended system takes as input a controversial topic, a stance, and a set of morals. it retrieves a set of argumentative texts, filters the ones conveying the input morals, and finally phrases an argument holding the given stance on the given topic focusing on the given morals. to identify morals in texts, we rely on distant supervision: we use the reddit dataset of @xcite , which contains argumentative texts with annotated aspects, along with the moral-to-concept lexicon of @xcite for the automatic mapping from aspects to morals. then, we train a bert-based classifier, achieving high performance on the moral dataset of @xcite compared to ablation baselines. to assess the effect of morally framed arguments on a particular audience, we consider liberals and conservatives as alternative audiences. we study the question of whether morally loaded arguments are more effective on a specific audience. additionally, we investigate whether differently-framed moral arguments (targeting different morals) vary in their effect on liberals and conservatives. in line with el @xcite , we design a user study to answer both questions. our study separately asks three liberals and three conservatives to rank different arguments on specific controversial issues based on how effective they were in challenging or empowering the audience's stance. the results suggest that, when arguments challenge the stance, the morally framed ones are generally more effective, especially for the conservative audience. we find that liberals value arguments that focus on their own morals (care and fairness) the most, especially when their stance is challenged. although conservatives also value respective arguments, a focus on typically conservative morals (loyalty, authority, and purity) becomes more relevant to them when their stance is challenged. despite the limited size of our user study, these findings hint at the importance of utilizing morals to craft more effective arguments. the code, the trained model, and the data are made publicly available. 2 the contributions of this work can be summarized as follows: prior beliefs in argumentation studying persuasiveness in argumentation is an active field of research. besides planning argument content, 2 https://github.com/webis-de/acl-22 structure, and style (wachsmuth et al., 2018), researchers also considered audience-based features to model persuasiveness. among these, both durmus and cardie (2018) and el baff et al. (2020) study how user factors such as religion and political background affect persuasiveness. al khatib et al. (2020) demonstrated that user-based features reflecting beliefs, characteristics, and personality could increase the predictability of argument persuasiveness. moreover, lukin et al. (2017) demonstrated that persuasiveness correlates with users' personality traits. the moral foundation theory offers another conceptual model of understanding human judgments in daily life @xcite . according to the theory, humans subconsciously adhere to five basic moral foundations when judging controversial issues: fairness (importance of justice, rights, and equality), care (being kind, and avoiding harm), loyalty (self-sacrifice, solidarity, belongingness), authority (respect to traditions and hierarchy), and purity (sacredness of religion and human). based on this theory, the disagreement between liberals and conservatives can be explained by the moral gap between the two parties. while liberals rely mainly on care and fairness (so-called individualizing morals) in their assessment of controversial issues, conservatives consider all moral foundations more evenly, somewhat skewed towards the binding morals though, that is, loyalty, authority, and purity @xcite . several studies provided evidence of the robustness of the moral foundation theory in understanding people's behaviors and decisions @xcite @xcite . for computational purposes, @xcite annotated moral foundations in arguments and analyzed the correlation between morals and argument quality. we use their corpus in our experiments to evaluate our moral classifier. argument generation argument generation approaches have been proposed for a spectrum of different tasks. @xcite and @xcite worked on counter-argument generation, tackling the task by rebutting an argument's conclusion or by undermining one of its weak premises, respectively. others opposed a given claim @xcite , generated argumentative claims on a given topic controlled for certain aspects @xcite , or reconstructed implicit conclusions (alshomary et al., 2020; @xcite . however, none of these approaches considered generating augmentative texts that target a specific audience. recently, @xcite introduced the task of belief-based claim generation, aiming to generate claims on a given topic that match a given target audience. as a model of beliefs, they considered people's stances on big issues. while they demonstrated the feasibility of encoding beliefs into claims, they did not study the effectiveness of these claims on an audience. another driver in the field of argument generation is project debater by @xcite . project debater is an end-to-end system for argument mining, retrieval, and generation. the system relies on a hybrid approach consisting of retrieval, mining, clustering, and rephrasing components. in their manual evaluation, @xcite observe a competent quality of generated arguments compared to those crafted by humans. to generate morally framed arguments, we extend their system. relying on project debater helps us alleviate confounding effects of argument quality and, so, to focus on testing whether moral utilization affects the audience. ## moral foundation classification our proposed system relies on the ability to identify morals in arguments automatically. existing approaches to mining morals from texts are either lexicon-based or machine learning-based. a number of datasets with morals have been constructed for domains such as social media or news articles. for argumentative texts, @xcite manually annotated a small dataset of 220 arguments, which is only suitable for evaluation. we, therefore, decided to develop a moral foundation classifier based on data collected automatically using distant supervision, as explained in the following. ## the moral debater system this section describes the system that we developed to study the effect of morally framed arguments on the audience. our design extends the capabilities available via the project debater api with the moral foundation classifier from section 3. as input, it takes a controversial topic (say, "globalization"), a stance on the topic (say, pro), and a set of morals to be targeted (say, loyalty, authority, and purity). then, it determines a collection of claims and evidence on the given topic that focus ## studying the effect of moral framing to evaluate our hypothesis on the effect of morally framed arguments, we carried out a user study with two opposing target audiences, liberals and conservatives. our primary goal was to investigate whether morally framed arguments are more effective than uncontrolled ones. additionally, we sought to determine whether differently-framed arguments affect liberals and conservatives differ-ently. in the following, we report on this study. ## discussion and limitations we explicitly acknowledge the limited number of liberals and conservatives that we recruited in our final user study, who might not represent the whole population. since the low sample size affects the our approach is limited by its capability to retrieve argumentative texts that discuss the topic from different moral perspectives. in the retrieval component, we have focused on obtaining sentences containing precisely the addressed topic. a more elaborated approach could broaden the search to relevant topics through topic expansion. further, we could use project debater only through the predefined api, restricting the way we integrate the moral tagging of sentences. ideally, it would be performed during indexing; then, choosing the morals to focus on could be defined as part of the queries. also, it is noteworthy that our moral classifier is trained on an automatically annotated dataset which may have limited its performance. still, we demonstrated that it is feasible to tune arguments automatically to target certain morals and that such arguments tend to be more effective. moreover, our results indicate that differently framed arguments have a different effect on different audiences. this opens opportunities for generating more effective arguments when targeting a specific audience, bridging the gap between disagreeing parties by focusing on the shared beliefs. ## conclusion in this work, we have proposed an extension of project debater that generates morally framed arguments. due to the lack of training data for classifying morals, we have used distant supervision to collect a large set of argumentative sentences automatically annotated for moral foundations. training a bert-based classifier on the data yielded stateof-the-art results. we have integrated the classifier with functionalities of project debater to tune arguments tuned towards specific morals. according to our user study, arguments with morals relevant to liberals are more effective than arguments without any control of morals. also, conservatives were more affected by moral arguments. our results demonstrate the feasibility of generating morally framed arguments. by focusing on shared morals, we believe that a respective system helps bridge disagreement of opposing audiences in practice.