Mining, Assessing, and Improving Arguments in NLP and the Social Sciences (2023)

Abstract: Computational argumentation is an interdisciplinary research field, connecting Natural Language Processing (NLP) to other disciplines such as the social sciences. This tutorial will focus on a task that recently got into the center of attention in the community: argument quality assessment, that is, what makes an argument good or bad? We structure the tutorial along three main coordinates: (1) the notions of argument quality across disciplines (how do we recognize good and bad arguments?), (2) the modeling of subjectivity (who argues to whom; what are their beliefs?), and (3) the generation of improved arguments (what makes an argument better?). The tutorial highlights interdisciplinary aspects of the field, ranging from the collaboration of theory and practice (e.g., in NLP and social sciences), to approaching different types of linguistic structures (e.g., social media versus parliamentary texts), and facing the ethical issues involved (e.g., how to build applications for the social good). A key feature of this tutorial is its interactive nature: We will involve the participants in two annotation studies on the assessment and the improvement of quality, and we will encourage them to reflect on the challenges and potential of these tasks.

## introduction computational argumentation is a field encompassing varying tasks on the automated analysis and synthesis of natural language arguments. until recently, research in natural language processing (nlp) mostly dealt with argument mining (am), that is, the identification of argumentative claims that convey a stance towards some controversial issue, along with evidence given as reasons for the claims. am has been studied for various genres @xcite @xcite and argument models @xcite @xcite . as @xcite point out, the "reason giving" function of argumen-tation is what makes am specific: "although opinion mining and sentiment analysis provide techniques that are proving to be enormously successful [...] they can only tell us what opinions are being expressed and not why people hold the opinions they do". reason giving, however, is only one of two main functions of argumentation, the other being persuasion. the dynamics of these "two souls" of argumentation are complex and the balance between reason giving and persuasion varies depending on the communication setting. ## diversity we believe that exposing the students to the deliberative perspective of argumentation will be fruitful and enriching, as it might not be known to the typical *cl audience. it is our goal that participants leave our tutorial having learned the value of taking multiple disciplinary perspectives into account, even in a rather technical (logic-and nlp-oriented) subject such as computational argumentation. besides, our focus on subjectivity and personal argumentation as positive features (and not bugs) brings individuals and their differences at center stage, contributing to inclusivity in the field. ## learning outcomes this introductory tutorial aims to offer an elaborate, systematic, and interdisciplinary understanding of the assessment and improvement of the quality of natural language arguments: part iii (60 min.) interactive session 1 the tutorial targets both participants who are new to the field of computational argumentation and those who need a comprehensive overview of techniques and applications. as the tutorial is interdisciplinary by design, it is also of interest to participants from a social sciences background who hope to integrate their knowledge within nlp. finally, we expect the tutorial to attract attention from people interested in nlp techniques that currently impact the social and political world, in general. basic knowledge of linguistics and computational linguistics is required. a general interest in the collaboration between nlp and social sciences is expected; relevant material, however, will be introduced without requiring prior knowledge.