Argument Labeling Research Articles

On probabilistic argumentation and subargument-completeness

Abstract Probabilistic argumentation combines probability theory and formal models of argumentation. Given an argumentation graph where vertices are arguments and edges are attacks or supports between arguments, the approach of probabilistic labellings relies on a probability space where the sample space is any specific set of argument labellings of the graph, so that any labelling outcome can be associated with a probability value. Argument labellings can feature a label indicating that an argument is not expressed, and in previous work these labellings were constructed by exploiting the subargument-completeness postulate according to which if an argument is expressed then its subarguments are expressed and through the use of the concept of ‘subargument-complete subgraphs’. While the use of such subgraphs is interesting to compare probabilistic labellings with other works in the literature, it may also hinder the comprehension of a relatively simple framework. In this short communication, we revisit the construction of probabilistic labellings and demonstrate how labellings can be specified without reference to the concept of subargument-complete subgraphs. By doing so, the framework is simplified and yields a more natural model of argumentation.

On searching explanatory argumentation graphs

Cases or examples can be often explained by the interplay of arguments in favour or against their outcomes. This paper addresses the problem of finding explanations for a collection of cases where an explanation is a labelled argumentation graph consistent with the cases, and a case is represented as a statement labelling. The focus is on semi-abstract argumentation graphs specifying attack and subargument relations between arguments, along with particular complete argument labellings taken from probabilistic argumentation where arguments can be excluded. The framework and computational considerations lead to an iterated local iterative deepening depth-first search to collect ‘interesting’ semi-abstract argumentation graphs consistent with a collection of statement labellings. Finally, the framework is illustrated and evaluated with a proof-of-concept implementation.

On Three-Valued Acceptance Conditions of Abstract Dialectical Frameworks

Abstract Dialectical Frameworks (adfs) are generalizations of Dung's abstract argumentation frameworks (afs) where each argument has an associated acceptance condition expressed by a boolean formula. The resulting extension is robust enough not only to model the attack relation original to afs, but also others types of dependencies and interactions between arguments. A recent development in adfs proposed an alternative formalization involving three-valued acceptance conditions, connecting the original definitions of adfs to the concept of three-valued argument labellings, a core concept in computational argumentation literature. In this paper, we revise some of the main semantics defined under this three-valued approach and prove our definitions hold equivalence to well-known semantics of afs.

Labellings for assumption-based and abstract argumentation

The semantics of Assumption-Based Argumentation (ABA) frameworks are traditionally characterised as assumption extensions, i.e. sets of accepted assumptions. Assumption labellings are an alternative way to express the semantics of flat ABA frameworks, where one of the labels in, out, or undec is assigned to each assumption. They are beneficial for applications where it is important to distinguish not only between accepted and non-accepted assumptions, but further divide the non-accepted assumptions into those which are clearly rejected and those which are neither accepted nor rejected and thus undecided. We prove one-to-one correspondences between assumption labellings and extensions for the admissible, grounded, complete, preferred, ideal, semi-stable and stable semantics. We also show how the definition of assumption labellings for flat ABA frameworks can be extended to assumption labellings for any (flat and non-flat) ABA framework, enabling reasoning with a wider range of scenarios. Since flat ABA frameworks are structured instances of Abstract Argumentation (AA) frameworks, we furthermore investigate the relation between assumption labellings for flat ABA frameworks and argument labellings for AA frameworks. Building upon prior work on complete assumption and argument labellings, we prove one-to-one correspondences between grounded, preferred, ideal, and stable assumption and argument labellings, and a one-to-many correspondence between admissible assumption and argument labellings. Inspired by the notion of admissible assumption labellings we introduce committed admissible argument labellings for AA frameworks, which correspond more closely to admissible assumption labellings of ABA frameworks than admissible argument labellings do.

A method based on rules and machine learning for logic form identification in Spanish

AbstractLogic Forms (LF) are simple, first-order logic knowledge representations of natural language sentences. Each noun, verb, adjective, adverb, pronoun, preposition and conjunction generates a predicate. LF systems usually identify the syntactic function by means of syntactic rules but this approach is difficult to apply to languages with a high syntax flexibility and ambiguity, for example, Spanish. In this study, we present a mixed method for the derivation of the LF of sentences in Spanish that allows the combination of hard-coded rules and a classifier inspired on semantic role labeling. Thus, the main novelty of our proposal is the way the classifier is applied to generate the predicates of the verbs, while rules are used to translate the rest of the predicates, which are more straightforward and unambiguous than the verbal ones. The proposed mixed system uses a supervised classifier to integrate syntactic and semantic information in order to help overcome the inherent ambiguity of Spanish syntax. This task is accomplished in a similar way to the semantic role labeling task. We use properties extracted from the AnCora-ES corpus in order to train a classifier. A rule-based system is used in order to obtain the LF from the rest of the phrase. The rules are obtained by exploring the syntactic tree of the phrase and encoding the syntactic production rules. The LF algorithm has been evaluated by using shallow parsing with some straightforward Spanish phrases. The verb argument labeling task achieves 84% precision and the proposed mixed LFi method surpasses 11% a system based only on rules.

The Chinese Discourse TreeBank: a Chinese corpus annotated with discourse relations

The paper presents the Chinese Discourse TreeBank, a corpus annotated with Penn Discourse TreeBank style discourse relations that take the form of a predicate taking two arguments. We first characterize the syntactic and statistical distributions of Chinese discourse connectives as well as the role of Chinese punctuation marks in discourse annotation, and then describe how we design our annotation strategy procedure based on this characterization. The Chinese-specific features of our annotation strategy include annotating explicit and implicit discourse relations in one single pass, defining the argument labels on semantic, rather than syntactic, grounds, as well as annotating the semantic type of implicit discourse relations directly. We also introduce a flat, 11-valued semantic type classification scheme for discourse relations. We finally demonstrate the feasibility of our approach with evaluation results.

Let’s See a Boy and a Balloon: Argument Labels and Syntactic Frame in Verb Learning

It is by now well established that toddlers use the linguistic context in which a new word—and particularly a new verb—appears to discover aspects of its meaning. But what aspects of the linguistic context are most useful? To begin to investigate this, we ask how 2-year-olds use two sources of linguistic information that are known to be useful to older children and adults in verb guessing tasks: syntactic frame and the semantic content available in the noun phrases labeling the verb’s arguments. We manipulate the linguistic contexts in which we present novel verbs to see how they use these two sources of information, both separately and in combination, to acquire the verb’s meaning. Our results reveal that, like older children and adults, toddlers make use of both syntactic frame and semantically contentful argument labels to acquire verb meaning. But toddlers also require these two sources of information to be packaged in a particular way, into a single sentence that identifies “who did what to whom.”

A labelling approach for ideal and stage semantics

In this document, we describe the concepts of ideal semantics and stage semantics for abstract argumentation in terms of argument labellings. The difference between the traditional extensions approach and the labelling approach is that where the former only identifies the sets of accepted arguments, the latter also identifies the rejected arguments as well as the arguments that are neither accepted nor rejected. So far, the labellings approach has been successfully applied to complete, grounded, preferred, stable and semi-stable semantics, as well as to the concept of admissibility. In the current paper, we continue this line of research by showing that ideal semantics and stage semantics can also be described in terms of argument labellings.

An argument game for stable semantics

In this paper, we present a discussion game for argumentation under stable semantics. Our work is inspired by Vreeswijk and Prakken, who have defined a similar game for preferred semantics. In the current paper, we restate Vreeswijk and Prakken's work using the approach of argument labellings and then show how it can be adjusted for stable semantics. The nature of the resulting argument game is somewhat unusual, since stable semantics does not satisfy the property of relevance.

Semantic Role Labeling: An Introduction to the Special Issue

Semantic role labeling, the computational identification and labeling of arguments in text, has become a leading task in computational linguistics today. Although the issues for this task have been studied for decades, the availability of large resources and the development of statistical machine learning methods have heightened the amount of effort in this field. This special issue presents selected and representative work in the field. This overview describes linguistic background of the problem, the movement from linguistic theories to computational practice, the major resources that are being used, an overview of steps taken in computational systems, and a description of the key issues and results in semantic role labeling (as revealed in several international evaluations). We assess weaknesses in semantic role labeling and identify important challenges facing the field. Overall, the opportunities and the potential for useful further research in semantic role labeling are considerable.