B ayesian Argumentation-Scheme Networks: A Probabilistic Model of Argument Validity Facilitated by Argumentation Schemes (2021)

Abstract: We propose a methodology for representing the reasoning structure of arguments using Bayesian networks and predicate logic facilitated by argumentation schemes. We express the meaning of text segments using predicate logic and map the boolean values of predicate logic expressions to nodes in a Bayesian network. The reasoning structure among text segments is described with a directed acyclic graph. While our formalism is highly expressive and capable of describing the informal logic of human arguments, it is too open-ended to actually build a network for an argument. It is not at all obvious which segment of argumentative text should be considered as a node in a Bayesian network, and how to decide the dependencies among nodes. To alleviate the difficulty, we provide abstract network fragments, called idioms, which represent typical argument justification patterns derived from argumentation schemes. The network construction process is decomposed into idiom selection, idiom instantiation, and idiom combination. We define 17 idioms in total by referring to argumentation schemes as well as analyzing actual arguments and fitting idioms to them. We also create a dataset consisting of pairs of an argumentative text and a corresponding Bayesian network. Our dataset contains about 2,400 pairs, which is large in the research area of argumentation schemes.

## introduction we propose to formalize the reasoning structure of an argument towards quantitative validity assessment. to assess the validity, we need to understand what kind of reasoning is applied. the following example is a discussion on whether the death penalty deters crime. p: we should introduce the death penalty to deter crime. q: criminologists do not believe that it deters crime. p justifies the introduction of the death penalty as a means to deter crime. q's objection is justified based on expert opinion. by understanding how the argument is justified, we can identify more specific issues that need to be validated. for example, we can ask whether the goal of deterring crime is worth achieving, or whether the criminologist's area of expertise is really relevant to the assertion. we use bayesian networks and predicate logic to represent the reasoning structure of an argument as shown in fig. 1 . we express the meaning of text segments using predicate logic. for example, 'the death penalty is a means to deter crime' is expressed as means_to(the death penalty, dettering crime). then, we represent the dependencies between text segments using a bayesian network. for example, whether or not a crime can be deterred depends on whether or not the death penalty has a deterrent effect on crime and whether or not the death penalty is actually introduced. this formalism is expressive and flexible to represent a variety of human justifications as it just models conditional dependencies. however, due to its flexibility, it is too open-ended to build a network for an argument. why do we use the predicate means_to for a node in the example network? how can we decide the dependencies among nodes? to facilitate a network construction, we prepare abstract network fragments, called idioms, which represent typical argument justification patterns derived from argumentation schemes @xcite we define an idiom for the justification of means to an end referring to practical reasoning. the dotted line in fig. 1 uses this idiom. by preparing idioms, the network construction process is decomposed into idiom selection, idiom instantiation, and idiom combination. we define 17 idioms in total by referring to argumentation schemes as well as analyzing actual arguments and fitting idioms to them. we also create a dataset consisting of pairs of an argumentative text and a corresponding bayesian network. the problem is that annotating argumentation schemes in argumentative texts is too expensive. in addition to identifying justification patterns, we need to represent the reasoning structure with bayesian networks, which is almost impossible. we solve the problem by adopting a procedure to generate a text from a built network inspired by overnight, the data building method in semantic parsing @xcite . as a result, we can make about 2,400 argument-network pairs, which is large in the research area of argumentation schemes. our dataset is publicly available at https://github.com/mynlp/basn . in summary, our contribution is twofold. ## bayesian argumentation-scheme networks our goal is to represent the reasoning structure of arguments with bayesian networks towards an argument validity assessment. fig. 2 shows the overall process of network construction. first, we prepare abstract bayesian network fragments which represent typical argument justification patterns derived from argumentation schemes. we call the abstract fragments network idioms. then, through the analysis of an argument, appropriate idioms are selected and embodied by assigning logical variables. we call the embodied fragments network instances. finally, by combining the network instances, we can construct a bayesian network for the entire argument. in the following, we first describe a formalism of an argument network. second, we explain how network idioms are defined and introduce three representative idioms. third, we provide network combination rules to construct an entire argument network. finally, we explain the advantages of our methodology. ## dataset creation we create a dataset consisting of pairs of relatively short english argument texts and their corresponding bayesian networks. our dataset not only helps us to train a machine learning model but also empirically shows that our approach can model the reasoning structure of actual arguments. as described in sec. 2.3, annotating argumentation schemes is very costly. in addition, our proposed method requires annotating a network structure. therefore, it is difficult to prepare a large amount of high-quality data by the procedure of giving an argument text and annotating a network. in this work, we adopt the procedure of generating an argument text from a pre-built network. ## conclusion and future work we propose a methodology for representing the reasoning structure of arguments using bayesian networks and predicate logic. we define 17 network idioms derived from argumentation schemes to facilitate the network construction by giving typical network patterns. we also create a large dataset consisting of 2,370 pairs of argument text and a corresponding bayesian network. in future work, we train a model for automatic network construction.