Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung



We approach aspect-based argument mining as a supervised machine learning task to classify arguments into semantically coherent groups referring to the same defined aspect categories. As an exemplary use case, we introduce the Argument Aspect Corpus-Nuclear Energy that separates arguments about the topic of nuclear energy into nine major aspects. Since the collection of training data for further aspects and topics is costly, we investigate the potential for current transformer-based few-shot learning approaches to accurately classify argument aspects. The best approach is applied to a British newspaper corpus covering the debate on nuclear energy over the past 21 years. Our evaluation shows that a stable prediction of shares of argument aspects in this debate is feasible with 50 to 100 training samples per aspect. Moreover, we see signals for a clear shift in the public discourse in favor of nuclear energy in recent years. This revelation of changing patterns of pro and contra arguments related to certain aspects over time demonstrates the potential of supervised argument aspect detection for tracking issue-specific media discourses.


Titel2022 Language Resources and Evaluation Conference, LREC 2022
Redakteure/-innenNicoletta Calzolari, Frederic Bechet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Jan Odijk, Stelios Piperidis
Herausgeber (Verlag)European Language Resources Association (ELRA)
ISBN (elektronisch)9791095546726
PublikationsstatusVeröffentlicht - 2022


ReiheLanguage Resources and Evaluation Conference (LREC)


Titel13th International Conference on Language Resources and Evaluation Conference, LREC 2022
Dauer20 - 25 Juni 2022

Externe IDs

ORCID /0000-0001-9756-6390/work/146644782



  • argument aspects, argument frames, argument mining, aspect-based argument mining, few-shot learning, nuclear energy discourse, text classification