Argumentation Semantics Research Articles

The rise, function, and fall of the σχῆμα Σοφόκλειον

The combination of the verb ἔχω with the active or middle aorist participle (e. g. ἔχω γράψας) acquires a limited degree of productivity as a periphrastic construction in classical Greek literature, notably tragedy (Sophocles, Euripides) and Herodotean prose. Traditionally, this so-called σχῆμα Σοφόκλειον has been regarded as a suppletive periphrasis stepping in for as yet unavailable synthetic perfect forms at a time when the (object-)resultative use of the perfect became increasingly widespread. However, a variety of distributional, formal, and semantic arguments speak against this theory. Instead, it is argued here that the σχῆμα Σοφόκλειον arose as a means of disambiguating resultative from narrative aorists, and that only its disappearance in the fourth century is directly related to the history of the perfect. Since it had always been confined to a relatively narrow range of linguistic registers and pragmatic contexts, the σχῆμα fell out of use as the perfect gradually strengthened its hold on the resultative domain.

Probabilistic causal bipolar abstract argumentation: an approach based on credal networks

The Bipolar Argumentation Framework approach is an extension of the Argumentation Framework. A Bipolar Argumentation Framework considers a support interaction between arguments, besides the attack interaction. As in the Argumentation Framework, some researches consider that arguments have a degree of uncertainty, which impacts on the degree of uncertainty of the extensions obtained from a Bipolar Argumentation Framework under a semantics. In these approaches, both the uncertainty of the arguments and of the extensions are modeled by means of precise probability values. However, in many real application domains there is a need for aggregating probability values from different sources so it is not suitable to aggregate such probability values in a unique probability distribution. To tackle this challenge, we use credal networks theory for modelling the uncertainty of the degree of belief of arguments in a BAF. We also propose an algorithm for calculating the degree of uncertainty of the extensions inferred by a given argumentation semantics. Moreover, we introduce the idea of modelling the support relation as a causal relation. We formally show that the introduced approach is sound and complete w.r.t the credal networks theory.

Notes from the Editor

Gaming Law ReviewVol. 27, No. 4 Notes from the EditorFree AccessNotes from the EditorSteve RuddockSteve RuddockSearch for more papers by this authorPublished Online:15 May 2023https://doi.org/10.1089/glr2.2023.29077.editorialAboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail With the release of the UK White Paper and the ongoing debate over sports betting advertisements in the U.S., the remainder of 2023 will likely be spent discussing responsible gambling policies. That should be a positive development, but considering the current debates raging, I fear the end result is an industry no closer to making a dent in problem gambling than it is today.So far as I can tell, the plan is to throw more money at the problem and rush through untested policies being hatched in a problem gambling bubble. Many of the ideas being peddled merely nibble around the edges and feel more like semantic arguments than actual solutions. If we look ahead ten years, will prohibiting “risk-free” or “can't lose” verbiage be a difference-maker? Will sterilizing how we talk about the subject—a “person experiencing gambling disorder” instead of a “problem gambler”—change the percentage or severity of gambling addiction? Is unsolicited outreach a beneficial policy?If these were headlines, applying Betteridge's Law seems appropriate.Increased funding and awareness are long overdue, but consumers deserve more than new academic terms, checking boxes, and knee-jerk policy decisions with the potential for severe, long-lasting implications that can be difficult to reverse.The recent successes on the responsible and problem gambling fronts read like a wish list from 1997. While the increases in research and available data are good, I often wonder if anyone truly understands what the data is saying, or even more worrisome, if it is saying anything at all.This is a topic I hope to discuss in upcoming issues.FiguresReferencesRelatedDetails Volume 27Issue 4May 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Steve Ruddock.Notes from the Editor.Gaming Law Review.May 2023.163-163.http://doi.org/10.1089/glr2.2023.29077.editorialPublished in Volume: 27 Issue 4: May 15, 2023PDF download

An argumentation semantics for rational human evaluation of arguments.

In abstract argumentation theory, many argumentation semantics have been proposed for evaluating argumentation frameworks. This article is based on the following research question: Which semantics corresponds well to what humans consider a rational judgment on the acceptability of arguments? There are two systematic ways to approach this research question: A normative perspective is provided by the principle-based approach, in which semantics are evaluated based on their satisfaction of various normatively desirable principles. A descriptive perspective is provided by the empirical approach, in which cognitive studies are conducted to determine which semantics best predicts human judgments about arguments. In this article, we combine both approaches to motivate a new argumentation semantics called SCF2. For this purpose, we introduce and motivate two new principles and show that no semantics from the literature satisfies both of them. We define SCF2 and prove that it satisfies both new principles. Furthermore, we discuss findings of a recent empirical cognitive study that provide additional support to SCF2.

A Principle-based Account of Self-attacking Arguments in Gradual Semantics

AbstractThe issue of how a semantics should deal with self-attacking arguments was always a subject of debate among argumentation scholars. A consensus exists for extension-based semantics because those arguments are always rejected (as soon as the semantics in question respects conflict-freeness). In case of gradual semantics, the question is more complex, since other criteria are taken into account. In this paper, we check the impact of those arguments by using a principle-based approach. Principles like self-contradiction and strong self-contradiction prescribe how to deal with self-attacking arguments. We show that they are incompatible with the well-known equivalence principle (which is satisfied by almost all the existing gradual semantics), as well as with some other principles (e.g. counting). This incompatibility was not studied until now and the class of semantics satisfying self-contradiction is under-explored. In the present paper, we explore that class of semantics. We show links and incompatibilities between several principles. We define a new general oriented argumentation semantics that satisfies (strong) self-contradiction and a maximal number of compatible principles. We introduce an iterative algorithm to calculate our semantics and prove that it always converges. We also provide a characterization of our semantics. Finally, we experimentally show that our semantics is computationally efficient.

The complexity landscape of claim-augmented argumentation frameworks

Claim-augmented argumentation frameworks (CAFs) provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective; they extend Dung AFs by associating a claim to each argument representing its conclusion. This additional layer offers various possibilities to generalize abstract argumentation semantics, i.e. the re-interpretation of arguments in terms of their claims can be performed at different stages in the evaluation of the framework: One approach is to perform the evaluation entirely at argument-level before interpreting arguments by their claims (inherited semantics); alternatively, one can perform certain steps in the process (e.g., maximization) already in terms of the arguments' claims (claim-level semantics). The inherent difference of these approaches not only potentially results in different outcomes but, as we will show in this paper, is also mirrored in terms of computational complexity. To this end, we provide a comprehensive complexity analysis of the four main reasoning problems with respect to claim-level variants of preferred, naive, stable, semi-stable and stage semantics and complete the complexity results of inherited semantics by providing corresponding results for semi-stable and stage semantics. Furthermore, we provide complexity results for these types of frameworks when restricted to specific graph classes and when parameterized by the number of claims within the framework. Moreover, we show that deciding, whether for a given framework the two approaches of a semantics coincide (concurrence) can be surprisingly hard, ranging up to the third level of the polynomial hierarchy.

A comprehensive account of the burden of persuasion in abstract argumentation

AbstractIn this paper, we provide a formal framework for modeling the burden of persuasion in legal reasoning. The framework is based on abstract argumentation, a frequently studied method of non-monotonic reasoning, and can be applied to different argumentation semantics; it supports burdens of persuasion with arbitrary many levels, and allows for the placement of a burden of persuasion on any subset of an argumentation framework’s arguments. Our framework can be considered an extension of related works that raise questions on how burdens of persuasion should be handled in some conflict scenarios that can be modeled with abstract argumentation. An open source software implementation of the introduced formal notions is available as an extension of an argumentation reasoning library. A theoretical analysis shows that our approach can be generalized to a novel method for the preference-based selection of extensions from argumentation frameworks.

The Interaction of Nominalisation and Compounding in Polish: On the Analysis of Listonosz ‘Mail Carrier’ and Korkociąg ‘Corkscrew’ in Construction Morphology

This paper compares two analyses of interfixal-paradigmatic formations in Polish, such as listonosz ‘mail carrier’ and korkociąg ‘corkscrew’. They are often interpreted as exocentric compounds consisting of a noun stem and a verb stem connected by a linking vowel, i.e. as [N+LV+V]N formations (Kurzowa; Booij, “Morphology”). The proposal defended here is the treatment of such lexemes as [N+LV+[V]N]N compounds, in which the right-hand verb stems are assumed to undergo conversion into nouns. Semantic arguments are presented in support of such an assumption. Moreover, differences are emphasised between properties of the interfixal-paradigmatic formations in question and features of exocentric compounds in which the verb stem stands in the initial position, e.g. moczymorda ‘drunkard’. The analysis adopted here in the framework of Construction Morphology involves the simultaneous application of two morphological operations, namely compounding and nominalisation (i.e. verb-to-noun conversion), in the formation of interfixal-paradigmatic lexemes. Such an assumption allows for the explanation of the phenomenon of “embedded productivity” (Booij, Construction Morphology).

A Boolean model for conflict-freeness in argumentation frameworks

<abstract><p>The Boolean models of argumentation semantics have been established in various ways. These models commonly translate the conditions of extension-based semantics into some constraints of the models. The goal of this work is to explore a simple method to build Boolean models for argumentation. In this paper, the attack relation is treated as an operator, and its value is calculated by the values of its target and source arguments. By examining the values of the attacks, a Boolean model of conflict-free sets is introduced. This novel method simplifies the existing ways by eliminating the various constraints. The conflict-free sets can be calculated by simply checking the values of the attacks.</p></abstract>

Framing events in the logic of verbal modification

I ask what a small set of modification data requires of clausal event semantics. Classic Davidsonian semantics posits that modifiers like "in the hallway" express properties of events, and expects that iterations of such modifiers will simply contribute additional conjuncts at logical form. The data I consider challenges this view, and others cast in the Davidsonian spirit, at least so long as we hope to preserve an important and plausible semantic principle, Role Exhaustion (Williams 2015). As I show, preserving the principle and accounting for the facts can be accomplished by adopting two independently-motivated sets of claims: first, that verbs introduce existential closure over their event argument, and modifiers take verb meanings as semantic arguments (Champollion 2015); second, that simple clauses have two layers of event description, "framing" and "framed" (Schein 2016). In the end, I sketch two possible extensions of the approach, towards the interpretation of temporal modification and negative perceptual reports.

Machine understanding surgical actions from intervention procedure textbooks

The automatic extraction of procedural surgical knowledge from surgery manuals, academic papers or other high-quality textual resources, is of the utmost importance to develop knowledge-based clinical decision support systems, to automatically execute some procedure’s step or to summarize the procedural information, spread throughout the texts, in a structured form usable as a study resource by medical students. In this work, we propose a first benchmark on extracting detailed surgical actions from available intervention procedure textbooks and papers. We frame the problem as a Semantic Role Labeling task. Exploiting a manually annotated dataset, we apply different Transformer-based information extraction methods. Starting from RoBERTa and BioMedRoBERTa pre-trained language models, we first investigate a zero-shot scenario and compare the obtained results with a full fine-tuning setting. We then introduce a new ad-hoc surgical language model, named SurgicBERTa, pre-trained on a large collection of surgical materials, and we compare it with the previous ones. In the assessment, we explore different dataset splits (one in-domain and two out-of-domain) and we investigate also the effectiveness of the approach in a few-shot learning scenario. Performance is evaluated on three correlated sub-tasks: predicate disambiguation, semantic argument disambiguation and predicate-argument disambiguation. Results show that the fine-tuning of a pre-trained domain-specific language model achieves the highest performance on all splits and on all sub-tasks. All models are publicly released.

A general approach to extension-based semantics in abstract argumentation

After a few decades of development, computational argumentation has become one of the active areas in AI. This paper proposes a general approach to deal with semantic related problems of abstract argumentation frameworks (AAFs, for short) and further develops the graded semantics presented by Grossi and Modgil. We pay attention not only to issues of various extension-based semantics at the object level but also the metatheoretical level. First, an alternative fundamental lemma is given, which generalizes the corresponding result due to Grossi and Modgil by relaxing the constraint on parameters. This lemma provides a new sufficient condition for preserving conflict-freeness and brings a Galois adjunction between admissible sets and complete extensions, which is of vital importance in constructing some special extensions in terms of iterations of the defense function. Applying such a lemma, some flaws in Grossi and Modgil's work are corrected, and the structural property, universal definability of various extension-based semantics and relationships among semantics are considered. Second, the operator ⋂D so-called reduced meet modulo an ultrafilter is presented, which is a simple but powerful tool in exploring infinite AAFs. The neutrality function and the defense function, which play central roles in Dung's abstract argumentation theory, are shown to be distributive over reduced meets modulo any ultrafilter. A variety of fundamental semantics for AAFs (including conflict-free, admissible, complete and stable semantics, etc) and some derived semantics are shown to be closed under this operator. Based on these facts, a number of applications of the operator ⋂D are considered, which show this operator can deal with various aspects of argumentation theory. In particular, we provide a simple and uniform method to prove the universal definability of a family of range related semantics. In addition to these results at the object level, this paper also explores ones at the metatheoretical level. To illustrate the universal applicability of the operator ⋂D as a tool for handling semantic related problems, we introduce the first order language FS(Ω) and establish a connection between ultraproducts of models in model theory and reduced meets of extensions modulo ultrafilters. On the basis of this, we characterize the extension-based semantics that is closed under the operator ⋂D in terms of the FS(Ω)-definability, which provides an example of utilizing model theory to explore the theoretical problems of AAFs and brings a series of metatheorems about extension-based semantics. In a word, our work shows that the operator ⋂D can deal with various problems related to various semantics, which suggests that it provides a general method to study AAFs.

Important Arguments Nomination Based on Fuzzy Labeling for Recognizing Plagiarized Semantic Text

Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when semantic analysis is involved. The Internet’s easy access to and copying of text content is one factor contributing to the growth of plagiarism. The present paper relates generally to text plagiarism detection. It relates more particularly to a method and system for semantic text plagiarism detection based on conceptual matching using semantic role labeling and a fuzzy inference system. We provide an important arguments nomination technique based on the fuzzy labeling method for identifying plagiarized semantic text. The suggested method matches text by assigning a value to each phrase within a sentence semantically. Semantic role labeling has several benefits for constructing semantic arguments for each phrase. The approach proposes nominating for each argument produced by the fuzzy logic to choose key arguments. It has been determined that not all textual arguments affect text plagiarism. The proposed fuzzy labeling method can only choose the most significant arguments, and the results were utilized to calculate similarity. According to the results, the suggested technique outperforms other current plagiarism detection algorithms in terms of recall, precision, and F-measure with the PAN-PC and CS11 human datasets.

Dependency and Span, Cross-Style Semantic Role Labeling on PropBank and NomBank

The latest developments in neural semantic role labeling (SRL) have shown great performance improvements with both the dependency and span formalism/styles. Although the two styles share many similarities in linguistic meaning and computation, most previous studies focus on a single style. In this article, we define a new cross-style semantic role label convention and propose a new cross-style joint optimization model designed around the most basic linguistic meaning of a semantic role. Our work provides a solution to make the results of the two styles more comparable and allowing both formalisms of SRL to benefit from their natural connections in both linguistics and computation. Our model learns a general semantic argument structure and is capable of outputting in either style. Additionally, we propose a syntax-aided method to uniformly enhance the learning of both dependency and span representations. Experiments show that the proposed methods are effective on both span and dependency SRL benchmarks.

Explaining Semantics and Extension Membership in Abstract Argumentation

This paper explores the computation of explanations in the specific context of abstract argumentation. The explanations that we define are designed to be visual, in the sense that they take the form of subgraphs of the argumentation graph. Moreover, these explanations rely on the modular aspects of abstract argumentation semantics and can consequently be either aggregated or decomposed. We investigate graph properties of these explanations, and their adequacy to desirable explanatory criteria.

Сочетаемость аргументов разных функциональных групп в научных текстах

The study aims to determine the compatibility of arguments from different functional groups in a collection of scientific texts. The study is novel in that it develops a functional classification of argumentation schemes and identifies the features of using arguments from different functional groups in the collection of Russian-language scientific texts (it is the first time that such an analysis of functional compatibility of arguments has been carried out both for texts of the scientific genre and for texts in Russian). Based on a comparative analysis of the semantics of arguments and the functional features of their use, a classifi-cation of argumentation schemes has been developed differentiating four methods of proof (from authority, from practical value, through elaboration or causal analysis). The use of arguments from four groups has been investigated using a set of 1030 reasoning sequences extracted from expertly annotated scientific papers on linguistics and computer technology. It has been shown that the analysed papers are characterised by an active combination of arguments from different functional groups with their uneven positional arrangement in some sequences, depending on the emphasis in the proof. The work includes the following parts: argumentation modelling, a functional comparison of argumentation schemes, presentation of reasoning through functional blocks, a compatibility analysis of such arguments.

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.

Recursion in Abstract Argumentation is Hard --- On the Complexity of Semantics Based on Weak Admissibility

We study the computational complexity of abstract argumentation semantics based on weak admissibility, a recently introduced concept to deal with arguments of self-defeating nature. Our results reveal that semantics based on weak admissibility are of much higher complexity (under typical assumptions) compared to all argumentation semantics which have been analysed in terms of complexity so far. In fact, we show PSPACE-completeness of all non-trivial standard decision problems for weak-admissible based semantics. We then investigate potential tractable fragments and show that restricting the frameworks under consideration to certain graph-classes significantly reduces the complexity. We also show that weak-admissibility based extensions can be computed by dividing the given graph into its strongly connected components (SCCs). This technique ensures that the bottleneck when computing extensions is the size of the largest SCC instead of the size of the graph itself and therefore contributes to the search for fixed-parameter tractable implementations for reasoning with weak admissibility.

The metaphysical burden of Millianism

The Millian semantic view of names relies on a metaphysical view of names—often given the label ‘common currency conception’ (‘CCC’)—on which the names of distinct individuals count as distinct names. While even defenders of the Millian view admit that the CCC ‘does not agree with the most common usage’ (Kripke in Naming & Necessity, Harvard University Press, 1980), I will argue further that the CCC makes names exceptional amongst the class of linguistic expressions: if the CCC is correct, then names must have a sui-generis metaphysical nature, distinct from the metaphysics of every other kind of linguistic expression. Such metaphysical exceptionalism would be justified if the Millian view had a clear, uncontested theoretical advantage over its rivals. However, in the context of a semantic debate about names in which the closest competitors of the Millian view—the Predicate view and Indexicalism—do not result in such exceptionalism, it counts as a strike against the Millian view.

On Interdisciplinary Studies of a New Generation of Artificial Intelligence and Logic

A new generation of artificial intelligence (NGAI), currently based on big data and machine learning, follows a path of connectionism. Although this path achieves huge success in data-intensive applications under closed environments, there are some bottleneck problems, including a lack of explainability, the difficulty of ethical alignment, the weakness of cognitive reasoning, etc. To address these problems inevitably involves the depiction of information from an open, dynamic and real environment and the modeling of human reasoning and explanation mechanisms. Formal argumentation is a general formalism for modeling various types of knowledge representation and reasoning in a context of disagreement, and is flexible enough to incorporate other types of knowledge for decisionmaking, such as preferences, weights, and probabilities. Meanwhile, there are various approaches for efficient computation of argumentation semantics by exploiting the locality and modularity of argumentation, and for providing explanations based on arguments and dialogues. The organic combination of formal argumentation with existing big data and machine learning techniques can be expected to break through some existing technical bottlenecks and facilitate the sustainable development of NGAI.