Diversity Aware Relevance Learning for Argument Search

Fromm, Michael; Berrendorf, Max; Obermeier, Sandra; Seidl, Thomas; Faerman, Evgeniy

doi:10.1007/978-3-030-72240-1_24

Cited by 1 publication

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High-level applications such as Argument Retrieval (Wachsmuth et al, 2017b;Fromm et al, 2021;Dumani and Schenkel, 2019;Dumani et al, 2020;Stab et al, 2018a) and autonomous debating systems (Slonim et al, 2021) require reliable Argument Quality (AQ) models to select strong arguments among the relevant ones.…”

Section: Generalization Across Argumentmentioning

confidence: 99%

“…AM is helpful over various scenarios, reaching from legal reasoning (Wyner et al, 2010;Walker et al, 2014;Poudyal et al, 2020;Villata et al, 2020) to supporting the decision-making process of politicians (Lippi and Torroni, 2016a;Haddadan et al, 2019;Duthie et al, 2016a;Menini et al, 2017;Lippi and Torroni, 2016b;Awadallah et al, 2012). Thus, there is a flurry of works on identification of arguments from text (Stab et al, 2018b;Fromm et al, 2019;Trautmann et al, 2020) and retrieval of them (Wachsmuth et al, 2017b;Fromm et al, 2021;Dumani and Schenkel, 2019;Dumani et al, 2020;Stab et al, 2018a). Since arguments often have to be weighed against each other, a central property of arguments is their Argument Quality (AQ) or convincingness, i.e., their (perceived) strength.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards a Holistic View on Argument Quality Prediction

Fromm¹,

Berrendorf²,

Reiml³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Argumentation is one of society's foundational pillars, and, sparked by advances in NLP and the vast availability of text data, automated mining of arguments receives increasing attention. A decisive property of arguments is their strength or quality. While there are works on the automated estimation of argument strength, their scope is narrow: they focus on isolated datasets and neglect the interactions with related argument mining tasks, such as argument identification, evidence detection, or emotional appeal. In this work, we close this gap by approaching argument quality estimation from multiple different angles: Grounded on rich results from thorough empirical evaluations, we assess the generalization capabilities of argument quality estimation across diverse domains, the interplay with related argument mining tasks, and the impact of emotions on perceived argument strength. We find that generalization depends on a sufficient representation of different domains in the training part. In zero-shot transfer and multi-task experiments, we reveal that argument quality is among the more challenging tasks but can improve others. Finally, we show that emotions play a minor role in argument quality than is often assumed. We publish our code at https://anonymous.4open.science/ r/kdd-holistic-view-aq-C0D8.

show abstract