Scientia Potentia E st— O n the Role of Knowledge in Computational Argumentation (2022)

Abstract: Despite extensive research efforts in recent years, computational argumentation (CA) remains one of the most challenging areas of natural language processing. The reason for this is the inherent complexity of the cognitive processes behind human argumentation, which integrate a plethora of different types of knowledge, ranging from topic-specific facts and common sense to rhetorical knowledge. The integration of knowledge from such a wide range in CA requires modeling capabilities far beyond many other natural language understanding tasks. Existing research on mining, assessing, reasoning over, and generating arguments largely acknowledges that much more knowledge is needed to accurately model argumentation computationally. However, a systematic overview of the types of knowledge introduced in existing CA models is missing, hindering targeted progress in the field. Adopting the operational definition of knowledge as any task-relevant normative information not provided as input, the survey paper at hand fills this gap by (1) proposing a taxonomy of types of knowledge required in CA tasks, (2) systematizing the large body of CA work according to the reliance on and exploitation of these knowledge types for the four main research areas in CA, and (3) outlining and discussing directions for future research efforts in CA.

## introduction the phenomenon of argumentation, a direct reflection of human reasoning in natural language, has fascinated scholars across societies and cul- * equal contribution. tures since ancient times @xcite . the computational modeling of human argumentation, commonly referred to as computational argumentation (ca), has evolved into one of the most prominent and at the same time most challenging areas in natural language processing (nlp) @xcite . ca encompasses several families of tasks and research directions, the main ones in nlp being argument mining, assessment, reasoning, and generation. although it bears some resemblance to other nlp tasks, such as opinion mining and natural language inference (nli), it is widely acknowledged to be of much higher difficulty than the other tasks @xcite . while opinion mining @xcite assesses stances towards entities or controversies by asking what the opinions are, ca provides answers to a more difficult question: why is the stance of an opinion holder the way it is? in a similar vein, while nli focuses on detecting simple entailments between statement pairs @xcite , ca addresses more complex reasoning scenarios that involve multiple entailment steps, often over implicit premises @xcite . ca targets reasoning processes that are only partially explicated in text. its mastery thus requires advanced natural language understanding capabilities and a substantial amount of background knowledge @xcite . for example, the assessment of an argument's quality not only depends on the actual content of an argumentative text or speech but also on social and cultural context, such as speaker and audience characteristics, including their individual values, ideologies, and relationships @xcite . such contextual information remains most often implicit. for any concrete ca task, we here refer to all information that is not explicitly provided as input to models tackling the task but is (potentially) useful for it and (in most cases) normative in nature as knowledge (we detail this notion in §3.1). although there is ample awareness of the need for integrating various types of knowledge in ca models in the research community, there is no systematic overview of the types of knowledge that existing models and solutions for the different ca tasks rely on. this impedes targeted progress in pressing subareas of ca, such as argument generation. while general surveys on ca (e.g., @xcite and its subareas (e.g., al @xcite represent good starting points for targeted research along these lines, they lack a systematic analysis of the roles that different types of knowledge play in different ca tasks. contributions. in this work, we aim to systematically inform the research community about the types of knowledge that have-or have not yet-been integrated into computational models in different ca tasks. for this purpose, we (1) propose a pyramid-like taxonomy systematizing the relevant types of knowledge. the pyramid is organized by knowledge specificity, from linguistic knowledge and world and topic knowledge to argumentation-specific and task-specific knowledge. starting from 162 ca publications, we (2) survey the existing body of work with respect to the level of integration of the various types of knowledge and respective methodology by which the knowledge of each type is integrated into models. to this end, we carry out an expert annotation study in which we manually label individual papers with the types from the knowledge pyramid. finally, we (3) identify trends and challenges in the four most prominent ca subareas (mining, assessment, reasoning, and generation), summarizing them into three key recommendations for future ca research: structure. we start with an overview of the field of ca and its four most prominent subareas ( §2). in §3, we describe our survey methodology, before we establish the knowledge pyramid and present the results of the survey with respect to the types of knowledge from the pyramid ( §4). on this basis, we summarize emerging trends ( §5) and offer recommendations for future progress in ca ( §6). the study of argumentation in western societies can be traced back to ancient greece. with the development of democracy and, thereby, the need to influence public decisions, the art of convincing others became an essential skill for successful participation in the democratic process (aristotle, ca. 350 b.c.e./ translated 2007). in that period, rhetorical theories also started appearing in eastern societies and cultures, such as nyaya sutra @xcite . since then, a plethora of phenomena in the realm of argumentation, such as fallacies @xcite and argumentation schemes @xcite , have been studied extensively, usually focusing on specific domains, such as science @xcite and law @xcite . with the growing amount of argumentative data available publicly in web debates, scientific articles, and other internet sources, the computational modeling of argumentation, computational argumentation (ca), gradually gained prominence and popularity in the nlp community. as depicted in figure 1 , ca can be divided into four main subareas that represent the main highlevel types of tasks being tackled with computational models: mining, assessment, reasoning, and generation. argument mining. argument mining deals with the extraction of argumentative structures from natural language text (e.g., @xcite . traditionally, it has been addressed with a pipeline of models each tackling one analysis task, most commonly component identification, component classification, and relation identification @xcite . the set of argument components and relations is defined by the selected underlying argument model which reflects the rhetorical, dialogical, or monological structure of argumentation @xcite . for instance, the model of @xcite , designed for the legal domain, encompasses six components: a claim with an optional qualifier, data (i.e., a fact supporting the claim) connected to the claim via a warrant (i.e., the reason why support is given) and its backing, and a rebuttal (i.e., a counterconsideration to the claim). relations model the support or attack of components (or arguments) by others, sometimes with more fine-grained subtypes @xcite . in contrast to argument reasoning (see below), the information needed for inferring argumentative relations is contained in the text. ## methodology in this section, we first provide the definition of knowledge upon which we base this work. then, we detail the methodology that we devised and pursued in order to organize the types of knowledge that ca approaches and models utilize. ## knowledge in argumentation as a result of our survey, we now introduce the argumentation knowledge pyramid, our proposed taxonomy encompassing four coarse-grained types of knowledge leveraged in ca. we then profile the large body of papers from the four ca subareas through the lens of the pyramid. ## emerging trends and discussion we now summarize the emerging trends and open challenges in the four ca areas, abstracted from our analyses of the use of knowledge types. ## conclusion motivated by the theoretical importance of knowledge in argumentation and by previous work pointing to the need for more research on incorporating advanced types of knowledge in computational argumentation, we have studied the role of knowledge in the body of research works in the field. in total, we surveyed 162 publications spanning the subareas of argument mining, assessment, reasoning, and generation. to organize the approaches described in these works, we proposed a pyramid-like knowledge taxonomy systematizing the types of knowledge according to their specificity, from basic linguistic to taskspecific knowledge. our survey yields important findings. many approaches employing advanced knowledge types (e.g., world and argumentation-specific knowledge) report empirical gains. still, reliance on such external knowledge types is far from uniform across ca areas: while exploitation of such knowledge is pervasive in argument reasoning and generation, it is far less present in argument mining. we hope that our findings lead to more systematic consideration of different knowledge sources for ca tasks. elena cabrio and serena villata. 2012. combining textual entailment and argumentation theory for supporting online debates interactions. in proceedings of the 50th annual meeting of the association for computational linguistics (volume 2: short papers), pages 208-212, jeju island, korea. association for computational linguistics.