Incorporating Topic Aspects for Online Comment Convincingness Evaluation

Gu, Yunfan; Wei, Zhongyu; Xu, Maoran; Fu, Hao; Liu, Yang; Huang, Xuanjing

doi:10.18653/v1/w18-5212

Cited by 6 publications

(5 citation statements)

References 18 publications

(15 reference statements)

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“…As a first step, Reuver et al (2021) show that crosstopic stance-classification with BERT (Devlin et al, 2018) produces mixed results depending on the topics, but misses the relations between the topics. Gu et al (2018) show that integrating the topic of an argument helps assessing its persuasiveness.…”

Section: Topics In Computational Argumentationmentioning

confidence: 99%

Topic Ontologies for Arguments

Ajjour¹,

Kiesel²,

Stein³

et al. 2023

Preprint

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Many computational argumentation tasks, like stance classification, are topic-dependent: the effectiveness of approaches to these tasks significantly depends on whether the approaches were trained on arguments from the same topics as those they are tested on. So, which are these topics that researchers train approaches on? This paper contributes the first comprehensive survey of topic coverage, assessing 45 argument corpora. For the assessment, we take the first step towards building an argument topic ontology, consulting three diverse authoritative sources: the World Economic Forum, the Wikipedia list of controversial topics, and Debatepedia. Comparing the topic sets between the authoritative sources and corpora, our analysis shows that the corpora topicswhich are mostly those frequently discussed in public online fora-are covered well by the sources. However, other topics from the sources are less extensively covered by the corpora of today, revealing interesting future directions for corpus construction.

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Section: Topics In Computational Argumentationmentioning

confidence: 99%

Topic Ontologies for Arguments

Ajjour¹,

Kiesel²,

Stein³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…As a first step, Reuver et al (2021) show that cross-topic stance-classification with BERT (Devlin et al, 2018) produces mixed results depending on the topics, but misses the relations between the topics. Gu et al (2018) show that integrating the topic of an argument helps assessing its persuasiveness.…”

Section: Topics In Computational Argumentationmentioning

confidence: 99%

Topic Ontologies for Arguments

Ajjour,

Kiesel,

Stein

et al. 2023

Findings of the Association for Computational Linguistics: EACL 2023

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Many computational argumentation tasks, such as stance classification, are topic-dependent: The effectiveness of approaches to these tasks depends largely on whether they are trained with arguments on the same topics as those on which they are tested. The key question is: What are these training topics? To answer this question, we take the first step of mapping the argumentation landscape with The Argument Ontology (TAO). TAO draws on three authoritative sources for argument topics: the World Economic Forum, Wikipedia's list of controversial topics, and Debatepedia. By comparing the topics in our ontology with those in 59 argument corpora, we perform the first comprehensive assessment of their topic coverage. While TAO already covers most of the corpus topics, the corpus topics barely cover all the topics in TAO. This points to a new goal for corpus construction to achieve a broad topic coverage and thus better generalizability of computational argumentation approaches.

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“…-Un des textes proposait une méthode pour évaluer automatiquement le niveau de persuasion des interventions dans un fil de commentaires (online comments convincingness evaluation) (Gu et al, 2018); -Un des textes proposait une méthode pour analyser et prédire automatiquement les arguments et le langage des campagnes de relations publiques dans un contexte de défense de réputation (predict the language of reputation defence) (Naderi et Graeme, 2018); -Un autre proposait un agent de dialogue argumentatif en mesure de débattre avec des personnes humaines sur des sujets complexes et controversés, agent nommé Dave the Debater (Thu Le et al, 2018); -Un article proposait une méthode d'AM pour générer automatiquement les prémisses et les conclusions d'une argumentation sur un thème précis, en intégrant des connaissances contextuelles et une compréhension plus large des sujets connexes, un défi dans l'AM (Lawrence et Reed, 2017a); -Les mêmes chercheurs proposaient, dans une autre conférence, une technique d'AM (Complex Argumentative Interaction (CAI)) pour reconstruire de façon formalisée la structure argumentative d'un débat mené à large échelle, par exemple celui des élections présidentielles américaines de 2016, utilisé comme corpus (Lawrence et Reed, 2017b); -Un dernier texte classifiait et organisait plus de 200 000 arguments politiques récoltés sur Internet concernant la possibilité d'une nouvelle constitution au Chili (Fierro et al, 2017).…”

Section: Champs D'applicationsunclassified

Langage, post-politique et automatisation : critique préventive de l’argumentation artificielle

Cossette¹

2021

ticetsociete

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This article discusses the concepts of post-political, algorithmic governance and language in relation to recent research in argumentation mining, a field of study within Big Data and computational linguistics. This article shows that studies in argumentation mining are particularly oriented towards political debate and offer methods to depoliticize the debate process, but are still incapable of grasping all of language's specificities.

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Incorporating Topic Aspects for Online Comment Convincingness Evaluation

Cited by 6 publications

References 18 publications

Topic Ontologies for Arguments

Topic Ontologies for Arguments

Topic Ontologies for Arguments

Langage, post-politique et automatisation : critique préventive de l’argumentation artificielle

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