Natural Language Arguments Research Articles

Pathos in Natural Language Argumentation: Emotional Appeals and Reactions

In this paper, we present a model of pathos, delineate its operationalisation, and demonstrate its utility through an analysis of natural language argumentation. We understand pathos as an interactional persuasive process in which speakers are performing pathos appeals and the audience experiences emotional reactions. We analyse two strategies of such appeals in pre-election debates: pathotic Argument Schemes based on the taxonomy proposed by Walton et al. (Argumentation schemes, Cambridge University Press, Cambridge, 2008), and emotion-eliciting language based on psychological lexicons of emotive words (Wierzba in Behav Res Methods 54:2146–2161, 2021). In order to match the appeals with possible reactions, we collect real-time social media reactions to the debates and apply sentiment analysis (Alswaidan and Menai in Knowl Inf Syst 62:2937–2987, 2020) method to observe emotion expressed in language. The results point to the importance of pathos analysis in modern discourse: speakers in political debates refer to emotions in most of their arguments, and the audience in social media reacts to those appeals using emotion-expressing language. Our results show that pathos is a common strategy in natural language argumentation which can be analysed with the support of computational methods.

Reflective equilibrium in logic

Among the areas of knowledge that the method of reflective equilibrium (RE) has been applied to is that of logical validity. According to RE in logic, we come to be justified in believing a (deductive) logical theory in virtue of establishing some state of equilibrium between our initial judgements over the validity of specific (natural language) arguments and the logical principles which constitute our logical theory. Unfortunately, however, while relatively popular, RE with regards to logical theorizing is underspecified. In particular, it’s unclear what constitute: (1) the relevant logical “data”, (2) logical theories, so that they can be suitably tested, and (3) the mechanisms under which such theories are tested. Considering the various options for how to interpret the position, we argue that in order to be workable the advocate of RE about logic must embrace what we call an operationalised, wide and communal version of RE with a rich understanding of logical theories. Fortunately for the advocate of RE, there is an available account of logic’s epistemology which possesses just these properties, logical predictivism. However, equally unfortunately, logical predictivism commits the advocate of RE to certain further claims that they have historically been weary of holding. Consequently, it is unclear whether RE about logic itself is a viable proposal, or rather a similar but distinct epistemology of logic.

Thou Shalt Not Squander Life – Comparing Five Approaches to Argument Strength

Abstract Different approaches analyze the strength of a natural language argument in different ways. This paper contrasts the dialectical, structural, probabilistic (or Bayesian), computational, and empirical approaches by exemplarily applying them to a single argumentative text (Epicureans on Squandering Life; Aikin & Talisse, 2019). Rather than pitching these approaches against one another, our main goal is to show the room for fruitful interaction. Our focus is on a dialectical analysis of the squandering argument as an argumentative response that voids an interlocutor’s right to assertion. This analysis addresses the pragmatic dimensions of arguing and implies an argument structure that is consistent with empirical evidence of perceived argument strength. Results show that the squandering argument can be evaluated as a (non-fallacious) ad hominem argument, which however is not necessarily stronger than possible arguments attacking it.

Inductive reasoning in minds and machines.

Induction-the ability to generalize from existing knowledge-is the cornerstone of intelligence. Cognitive models of human induction are largely limited to toy problems and cannot make quantitative predictions for the thousands of different induction arguments that have been studied by researchers, or to the countless induction arguments that could be encountered in everyday life. Leading large language models (LLMs) go beyond toy problems but fail to mimic observed patterns of human induction. In this article, we combine rich knowledge representations obtained from LLMs with theories of human inductive reasoning developed by cognitive psychologists. We show that this integrative approach can capture several benchmark empirical findings on human induction and generate human-like responses to natural language arguments with thousands of common categories and properties. These findings shed light on the cognitive mechanisms at play in human induction and show how existing theories in psychology and cognitive science can be integrated with new methods in artificial intelligence, to successfully model high-level human cognition. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Artificial Intelligence at the Service of Society to Analyse Human Arguments

Argument(ation) mining (AM) is an area of research in Artificial Intelligence (AI) that aims to identify, analyse and automatically generate arguments in natural language. In a pipeline, the identification and analysis of the arguments and their components (i.e. premises and claims) in texts and the prediction of their relations (i.e. attack and support) are then handled by argument-based reasoning frameworks so that, for example, fallacies and inconsistencies can be automatically identified. Recently, the field of argument mining has tackled new challenges, namely the evaluation of argument quality (e.g. strength, persuasiveness), natural language argument summarisation and retrieval, and natural language argument generation. In this paper, I discuss my main contributions in this area as well as some lines of future research. This paper is part of the AAAI-23 New Faculty Highlights.

The Quantified Argument Calculus and Natural Logic

The formalisation of natural language arguments in a formal language close to it in syntax has been a central aim of Moss's Natural Logic. I examine how the Quantified Argument Calculus (Quarc) can handle the inferences Moss has considered. I show that they can be incorporated in existing versions of Quarc or in straightforward extensions of it, all within sound and complete systems. Moreover, Quarc is closer in some respects to natural language than are Moss's systems---for instance, it does not use negative nouns. The process also sheds light on formal properties and presuppositions of some inferences it formalises. Directions for future work are outlined.

Reasoning on conflicting information: An empirical study of Formal Argumentation.

According to the Argumentative Theory, human reasoning has an argumentative function, which consists of devising and evaluating arguments for and against various claims. It is however unclear how humans handle conflicting claims they face in everyday life (i.e., “Bob is telling me that Alice is at the library” vs. “Charles is telling me that Alice is at home”). We here investigate human argumentative reasoning in the light of Formal Argumentation, a research field that develops formal methods to give a normative account of argumentation and reasoning about conflicting information. In Formal Argumentation, multiple argumentation semantics that allow selecting sets of jointly acceptable arguments have been proposed. Nonetheless, it is unclear which of these semantics predicts best how humans evaluate the acceptability of conflicting arguments. We conducted an empirical study in which 130 young adults judged natural language arguments. We instructed them to draw the attack relation between the given arguments and to evaluate the acceptability of each of these arguments. Our results show that human judgments on the existence and directionality of attacks between the arguments conform to theoretical predictions from Formal Argumentation. We further found out that some less well-known argumentation semantics predicted human evaluation better than the most well-known semantics. These findings support the cognitive plausibility of variants of Formal Argumentation and bring new insights into reasoning about conflicting information.

Logic, Probability, and Pragmatics in Syllogistic Reasoning.

Syllogistic reasoning lies at the intriguing intersection of natural and formal reasoning of language and logic. Syllogisms comprise a formal system of reasoning yet make use of natural language quantifiers (e.g., all, some) and invite natural language conclusions. The conclusions people tend to draw from syllogisms, however, deviate substantially from the purely logical system. Are principles of natural language understanding to blame? We introduce a probabilistic pragmatic perspective on syllogistic reasoning: We decompose reasoning with natural language arguments into two subproblems: language comprehension and language production. We formalize models of these processes within the Rational Speech Act framework and explore the pressures that pragmatic reasoning places on the production of conclusions. We test our models on a recent, large data set of syllogistic reasoning and find that the selection process of conclusions from syllogisms are best modeled as a pragmatic speaker who has the goal of aligning the beliefs of a naive listener with those of their own. We compare our model to previously published models that implement two alternative theories-Mental Models and Probability Heuristics-finding that our model quantitatively predicts the full distributions of responses as well as or better than previous accounts, but with far fewer parameters. Our results suggest that human syllogistic reasoning may be best understood not as a poor approximation to ideal logical reasoning, but rather as rational probabilistic inference in support of naturalcommunication.

Averaging Solution of Differential Equations as Value for Argumentation Graph with Similarity

In this paper, we present a new method to get the value of claims and relations of an argumentation graph. By the method, the values of claims and relations are determined by the average of the solutions of the differential equation derived from the argumentation graph. We show that the overhead of obtaining multiple solutions can be reduced by parallel execution and the values obtained by the method agree with our intuition. We can get a confidence interval of each value and a rough grouping of claims and relations by the method. We show the results for toy graphs, large artificial graphs, and a natural language argumentation graph.

An interface between natural language and abstract argumentation frameworks for real-time debate analysis

Participatory approaches are increasingly being used to plan policies, usually involving stakeholders with conflicting interests, visions and objectives. Participants are often passionate about their cause, especially in sensitive contexts such as their health or the environment for example. Whether involved in, or observing a debate, more often than not, it is hard to follow its progress; positions are unclear and arguments are unstructured often resulting in circular discussions. This is true both for participants in an ongoing debate hoping to reach a consensus as well as for those who, a posteriori, wish to understand what was discussed and how. However, at the current time, there are to our knowledge no tools to support real-time debates by allowing participants to visualize arguments and identifying opposing points of view in order to resolve conflicts. In order to fill this gap, we developed a model based on Dung's argumentation framework that we implemented in a tool called AIPA, along with a web-application to facilitate user interaction. AIPA allows one to formalize and visualize, in real-time, the arguments of the participants, to conduct inferences in order to identify acceptable arguments and to highlight conflicting ones. Furthermore, AIPA can be used a posteriori to summarize a debate in an easy to follow argument representation. The main contribution of AIPA is its ability to structure the debate by making the arguments chain traceable and transparent, in real-time and a posteriori, and this, with users with no expertise in argumentation. In this paper, we present AIPA along with two applications to illustrate its workings and benefits.

Metaphorical Argumentation

It is a fact that novel metaphorical utterances appear in natural language argumentation. It seems, moreover, that these put forward metaphorical propositions that can have different roles (data, warrants or claims) in argument structure. There can even be good argumentation which is indispensably metaphorical. However, not all metaphor theories provide an explanation of metaphorical meaning compatible with these claims. In this article, we explain the three main views on metaphorical meaning and show, analysing some examples, their consequences for metaphorical argumentation. Our analysis shows that only the cognitive view can explain that there are arguments which can only be generated using novel metaphors.

The ReCAP Project

Argumentation Machines search for arguments in natural language from information sources on the Web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular user query. The recap project is part of the Priority Program ratio and aims at novel contributions to and confluence of methods from information retrieval, knowledge representation, as well as case-based reasoning for the development of future argumentation machines. In this paper we summarise recent research results from the project. In particular, a new German corpus of 100 semantically annotated argument graphs from the domain of education politics has been created and is made available to the argumentation research community. Further, we discuss a comprehensive investigation in finding arguments and argument graphs. We introduce a probabilistic ranking framework for argument retrieval, i.e. for finding good premises for a designated claim. For finding argument graphs, we developed methods for case-based argument retrieval considering the graph structure of an argument together with textual and ontology-based similarity measures applied to claims, premises, and argument schemes.

Douglas Walton’ın Argüman Biçimleri Yaklaşımı

It can be said that the roots of the studies on the argumentation theory go back to rhetorical and dialectical studies. One of the sub-fields of the argumentation theory is argument schemes, used in everyday language and various fields within certain rules. Today, with the studies in the field of informal logic, argumentation schemes have aslo become an important field to study. The argumentation schemes approach also plays an important role in the study of artificial language at the intersection of the computer science area. In this article, we discussed Douglas Walton’s argumentation schemes approach. Douglas Walton's aim is to determine the natural language arguments used in various fields and make as a list of them and propose a classification for argumentation schemes. Douglas Walton, presents a model for the argumentation schemes. This model also emphasized the structure suitable for use in computer systems. Determination, analysis and evaluation of arguments are important topics for the arguments applying to computer systems. In this article, we discuss one of these systems, Thomas Gordon’s project, the Carneades Argumentation System. The Carneades Argumentation System is a system which can handle Douglas Walton's argumentation schemes. With the Carneades Argumentation System, it is possible to visualize arguments as an argument map. The visualization of an argumentation provides a concrete basis for philosophical discussions. It makes it easy for the user to determine the scheme of the current argument when applying the argument formats to mapping and evaluation systems such as Carneades Argumentation Systems. Determining the argument scheme allows the argument to be handled within a particular model. The aim of this article is to discuss the argumentation schemes which is one of the topics of study at the intersection of philosophical logic and computer sciences in which informal logic studies are developing today

Generalization in Legal Argumentation

ABSTRACTWhen interpreting a natural language argument that generalizes over a contextually relevant category, audiences are likely to activate the category prototype and transfer its characteristics onto category instances. A generalized argument can thus appear more (respectively less) persuasive than one mentioning a specific category instance, provided the argument’s claim is more (less) warranted for the prototype than for the instance (positive and negative prototype effect). To investigate this effect in legal contexts using mock-scenarios, professional and lay judges at Swedish courts evaluated the persuasiveness of arguments giving a generalized or a specific description of an eyewitness. The generalized version described the witness either as an alcohol-intoxicated person or as a child, while the specific version varied both the amount of alcohol consumed (two vs. five glasses of wine) and the child’s age (four vs. 12 years). To investigate the effect of legal expertise on argument selection, moreover, law and social science students evaluate the persuasiveness of both argument versions. Though we observed statistically significant prototype effects as well as expertise effects, results were mixed and sometimes ran counter to normative expectation.

When Expert Opinion Evidence Goes Wrong

This paper combines three computational argumentation systems to model the sequence of argumentation in a famous murder trial and the appeal procedure that followed. The paper shows how the argumentation scheme for argument from expert opinion can be built into a testing procedure whereby an argument graph is used to interpret, analyze and evaluate evidence-based natural language argumentation of the kind found in a trial. It is shown how a computational argumentation system can do this by combining argument schemes with argumentation graphs. Frighteningly, it is also shown by this example that when there are potentially confusing conflicting arguments from expert opinion, a jury can only too easily accept a conclusion prematurely before considering critical questions that need to be asked.

When expert opinion evidence goes wrong

This paper combines three computational argumentation systems to model the sequence of argumentation in a famous murder trial and the appeal procedure that followed. The paper shows how the argumentation scheme for argument from expert opinion can be built into a testing procedure whereby an argument graph is used to interpret, analyze and evaluate evidence-based natural language argumentation of the kind found in a trial. It is shown how a computational argumentation system can do this by combining argument schemes with argumentation graphs. Frighteningly, it is also shown by this example that when there are potentially confusing conflicting arguments from expert opinion, a jury can only too easily accept a conclusion prematurely before considering critical questions that need to be asked.

A Dialectical View on Conduction: Reasons, Warrants, and Normal Suasory Inclinations

When Carl Wellman (1971) introduced the reasoning-type conduction, he endorsed a dialectical view on natural language argumentation. Contemporary scholarship, by contrast, treats conductive argument predominantly on a product view. Not only did Wellman’s reasons for a dialectical view thus fall into disregard; a product-treatment of conduction also flouts the standard semantics of ‘argument’. Attempting to resolve these difficulties, our paper traces Wellman’s preference for a dialectical view to the role of defeasible warrants. These act as stand-ins for (parts of) value hierarchies that arguers of normal suasory inclination find acceptable. We also improve on extant ways of diagramming conduction and distinguish two of its structural variants.

Logic, Reasoning, Argumentation: Insights from the Wild

This article provides a brief selective overview and discussion of recent research into natural language argumentation that may inform the study of human reasoning on the assumption that an episode of argumentation issues an invitation to accept a corresponding inference. As this research shows, arguers typically seek to establish new consequences based on prior information. And they typically do so vis-a-vis a real or an imagined opponent, or an opponent-position, in ways that remain sensitive to considerations of context, audiences, and goals. Deductively valid inferences remain a limiting case of such reasoning. In view of these insights, it may appear less surprising that allegedly “irrational” behavior can regularly be produced in experimental settings that expose subjects to standardized reasoning tasks.

MODUS PONENS AND MODUS TOLLENS: THEIR VALIDITY/INVALIDITY IN NATURAL LANGUAGE ARGUMENTS

Abstract The precedent studies on the validity of Modus ponens and Modus tollens have been carried out with most regard to a major type of conditionals in which the conditional clause is a sufficient condition for the main clause. But we sometimes, in natural language arguments, find other types of conditionals in which the conditional clause is a necessary or necessary and sufficient condition for the main clause. In this paper I reappraise, on the basis of new definitions of Modus ponens and Modus tollens, their validity/invalidity in natural language arguments in consideration of all types of conditionals.

A probabilistic analysis of argument cogency

This paper offers a probabilistic treatment of the conditions for argument cogency as endorsed in informal logic: acceptability, relevance, and sufficiency (RSA). Treating a natural language argument as a reason-claim-complex, our analysis identifies content features of defeasible argument on which the RSA conditions depend, namely: (1) change in the commitment to the reason, (2) the reason’s sensitivity and selectivity to the claim, (3) one’s prior commitment to the claim, and (4) the contextually determined thresholds of acceptability for reasons and for claims. Results contrast with, and may indeed serve to correct, the informal understanding and applications of the RSA criteria concerning their conceptual (in)dependence, their function as update-thresholds, and their status as obligatory rather than permissive norms, but also show how these formal and informal normative approachs can in fact align.