%0 Conference Paper %1 600e42c23fb440fd9d27baebf16f6004 %A Jurkschat, Lena %A Wiedemann, Gregor %A Heinrich, Maximilian %A Ruckdeschel, Mattes %A Torge, Sunna %B 2022 Language Resources and Evaluation Conference, LREC 2022 %D 2022 %E Calzolari, Nicoletta %E Bechet, Frederic %E Blache, Philippe %E Choukri, Khalid %E Cieri, Christopher %E Declerck, Thierry %E Goggi, Sara %E Isahara, Hitoshi %E Maegaard, Bente %E Mariani, Joseph %E Mazo, Helene %E Odijk, Jan %E Piperidis, Stelios %I European Language Resources Association (ELRA) %K FIS_scads argument aspect-based aspects, classification discourse, energy few-shot frames, learning, mining, nuclear text %P 663--672 %T Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate %X 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.

@inproceedings{600e42c23fb440fd9d27baebf16f6004, abstract = {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.}, added-at = {2024-11-28T16:27:18.000+0100}, author = {Jurkschat, Lena and Wiedemann, Gregor and Heinrich, Maximilian and Ruckdeschel, Mattes and Torge, Sunna}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2e7f4cca37ad96e95764660e4b9fd57e2/scadsfct}, booktitle = {2022 Language Resources and Evaluation Conference, LREC 2022}, editor = {Calzolari, Nicoletta and Bechet, Frederic and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, Helene and Odijk, Jan and Piperidis, Stelios}, interhash = {cee0b99b1db55f18f90fb52b0c1bb714}, intrahash = {e7f4cca37ad96e95764660e4b9fd57e2}, keywords = {FIS_scads argument aspect-based aspects, classification discourse, energy few-shot frames, learning, mining, nuclear text}, language = {English}, note = {Publisher Copyright: {\textcopyright} European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.; 13th International Conference on Language Resources and Evaluation Conference, LREC 2022 ; Conference date: 20-06-2022 Through 25-06-2022}, pages = {663--672}, publisher = {European Language Resources Association (ELRA)}, series = {Language Resources and Evaluation Conference (LREC)}, timestamp = {2024-11-28T16:27:18.000+0100}, title = {Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate}, year = 2022 }