Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments

This article discusses the usefulness of Toulmin’s model of arguments as structuring an assessment of different types of wrongness in an argument. We discuss the usability of the model within a conversational agent that aims to support users to develop a good argument. Within the article, we present...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Behzad Mirzababaei, Viktoria Pammer-Schindler
Formato: article
Lenguaje:EN
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://doaj.org/article/cc6a553b2afb427f81b715aea8786f97
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:cc6a553b2afb427f81b715aea8786f97
record_format dspace
spelling oai:doaj.org-article:cc6a553b2afb427f81b715aea8786f972021-12-03T15:31:21ZDeveloping a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments2624-821210.3389/frai.2021.645516https://doaj.org/article/cc6a553b2afb427f81b715aea8786f972021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/frai.2021.645516/fullhttps://doaj.org/toc/2624-8212This article discusses the usefulness of Toulmin’s model of arguments as structuring an assessment of different types of wrongness in an argument. We discuss the usability of the model within a conversational agent that aims to support users to develop a good argument. Within the article, we present a study and the development of classifiers that identify the existence of structural components in a good argument, namely a claim, a warrant (underlying understanding), and evidence. Based on a dataset (three sub-datasets with 100, 1,026, 211 responses in each) in which users argue about the intelligence or non-intelligence of entities, we have developed classifiers for these components: The existence and direction (positive/negative) of claims can be detected a weighted average F1 score over all classes (positive/negative/unknown) of 0.91. The existence of a warrant (with warrant/without warrant) can be detected with a weighted F1 score over all classes of 0.88. The existence of evidence (with evidence/without evidence) can be detected with a weighted average F1 score of 0.80. We argue that these scores are high enough to be of use within a conditional dialogue structure based on Bloom’s taxonomy of learning; and show by argument an example conditional dialogue structure that allows us to conduct coherent learning conversations. While in our described experiments, we show how Toulmin’s model of arguments can be used to identify structural problems with argumentation, we also discuss how Toulmin’s model of arguments could be used in conjunction with content-wise assessment of the correctness especially of the evidence component to identify more complex types of wrongness in arguments, where argument components are not well aligned. Owing to having progress in argument mining and conversational agents, the next challenges could be the developing agents that support learning argumentation. These agents could identify more complex type of wrongness in arguments that result from wrong connections between argumentation components.Behzad MirzababaeiViktoria Pammer-SchindlerViktoria Pammer-SchindlerFrontiers Media S.A.articleToulmin’s model of argumentargument miningargument quality detectioneducational technologyeducational conversational agentElectronic computers. Computer scienceQA75.5-76.95ENFrontiers in Artificial Intelligence, Vol 4 (2021)
institution DOAJ
collection DOAJ
language EN
topic Toulmin’s model of argument
argument mining
argument quality detection
educational technology
educational conversational agent
Electronic computers. Computer science
QA75.5-76.95
spellingShingle Toulmin’s model of argument
argument mining
argument quality detection
educational technology
educational conversational agent
Electronic computers. Computer science
QA75.5-76.95
Behzad Mirzababaei
Viktoria Pammer-Schindler
Viktoria Pammer-Schindler
Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments
description This article discusses the usefulness of Toulmin’s model of arguments as structuring an assessment of different types of wrongness in an argument. We discuss the usability of the model within a conversational agent that aims to support users to develop a good argument. Within the article, we present a study and the development of classifiers that identify the existence of structural components in a good argument, namely a claim, a warrant (underlying understanding), and evidence. Based on a dataset (three sub-datasets with 100, 1,026, 211 responses in each) in which users argue about the intelligence or non-intelligence of entities, we have developed classifiers for these components: The existence and direction (positive/negative) of claims can be detected a weighted average F1 score over all classes (positive/negative/unknown) of 0.91. The existence of a warrant (with warrant/without warrant) can be detected with a weighted F1 score over all classes of 0.88. The existence of evidence (with evidence/without evidence) can be detected with a weighted average F1 score of 0.80. We argue that these scores are high enough to be of use within a conditional dialogue structure based on Bloom’s taxonomy of learning; and show by argument an example conditional dialogue structure that allows us to conduct coherent learning conversations. While in our described experiments, we show how Toulmin’s model of arguments can be used to identify structural problems with argumentation, we also discuss how Toulmin’s model of arguments could be used in conjunction with content-wise assessment of the correctness especially of the evidence component to identify more complex types of wrongness in arguments, where argument components are not well aligned. Owing to having progress in argument mining and conversational agents, the next challenges could be the developing agents that support learning argumentation. These agents could identify more complex type of wrongness in arguments that result from wrong connections between argumentation components.
format article
author Behzad Mirzababaei
Viktoria Pammer-Schindler
Viktoria Pammer-Schindler
author_facet Behzad Mirzababaei
Viktoria Pammer-Schindler
Viktoria Pammer-Schindler
author_sort Behzad Mirzababaei
title Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments
title_short Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments
title_full Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments
title_fullStr Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments
title_full_unstemmed Developing a Conversational Agent’s Capability to Identify Structural Wrongness in Arguments Based on Toulmin’s Model of Arguments
title_sort developing a conversational agent’s capability to identify structural wrongness in arguments based on toulmin’s model of arguments
publisher Frontiers Media S.A.
publishDate 2021
url https://doaj.org/article/cc6a553b2afb427f81b715aea8786f97
work_keys_str_mv AT behzadmirzababaei developingaconversationalagentscapabilitytoidentifystructuralwrongnessinargumentsbasedontoulminsmodelofarguments
AT viktoriapammerschindler developingaconversationalagentscapabilitytoidentifystructuralwrongnessinargumentsbasedontoulminsmodelofarguments
AT viktoriapammerschindler developingaconversationalagentscapabilitytoidentifystructuralwrongnessinargumentsbasedontoulminsmodelofarguments
_version_ 1718373175232823296