Deutsch
Bibtex
Cite as text
@Select Types{,
Journal = "Band-1",
Title= "Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach",
Author= "Thiemo Wambsganss, Nikolaos Molyndris, Matthias Söllner",
Doi= "https://doi.org/10.30844/wi_2020_c9-wambsganss",
Abstract= "Argument identification is the fundamental block of every Argumentation Mining pipeline, which in turn is a young upcoming field with multiple applications ranging from strategy support to opinion mining and news fact-checking. We developed a model, which is tackling the two biggest practical and academic challenges of the research field today. First, it addresses the lack of corpus-agnostic models and, second, it tackles the problem of human-labor-intensive NLP models being costly to develop. We do that by suggesting and implementing an easy-to-use solution that utilizes the latest advancements in natural language Transfer Learning. The result is a two-fold contribution: A system that delivers state-of-the-art results in multiple corpora and opens up a new way of academic advancement of the field through Transfer Learning. Additionally, it provides the architecture for an easy-to-use tool that can be used for practical applications without the need for domain-specific knowledge.
",
Keywords= "Argumentation Mining, Argument Identification, Transfer Learning, Natural Language Processing",
}
Thiemo Wambsganss, Nikolaos Molyndris, Matthias Söllner: Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach. Online: https://doi.org/10.30844/wi_2020_c9-wambsganss (Abgerufen 26.12.24)
Open Access
Abstract
Abstract
Argument identification is the fundamental block of every Argumentation Mining pipeline, which in turn is a young upcoming field with multiple applications ranging from strategy support to opinion mining and news fact-checking. We developed a model, which is tackling the two biggest practical and academic challenges of the research field today. First, it addresses the lack of corpus-agnostic models and, second, it tackles the problem of human-labor-intensive NLP models being costly to develop. We do that by suggesting and implementing an easy-to-use solution that utilizes the latest advancements in natural language Transfer Learning. The result is a two-fold contribution: A system that delivers state-of-the-art results in multiple corpora and opens up a new way of academic advancement of the field through Transfer Learning. Additionally, it provides the architecture for an easy-to-use tool that can be used for practical applications without the need for domain-specific knowledge.
Keywords
Schlüsselwörter
Argumentation Mining, Argument Identification, Transfer Learning, Natural Language Processing
References
Referenzen
1. Stab, C., Gurevych, I.: Identifying Argumentative Discourse Structures in Persuasive Essays. 46–56 (2014).
2. Moens, M., Boiy, E., Reed, C.: Automatic Detection of Arguments in Legal Texts. (2007).
3. Boltuži, F., Šnajder, J.: Back Up Your Stance : Recognizing Arguments in Online Discussions. 1–43 (2014).
4. Dusmanu, M., Cabrio, E., Villata, S.: Argument Mining on Twitter: Arguments, Facts and Sources. 2317–2322 (2018).
5. Pan, S.J., Yang, Q.: A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng. 1–15 (2009).
6. Ferrara, A., Montanelli, S., Petasis, G.: Unsupervised Detection of Argumentative Units though Topic Modeling Techniques. 97–107 (2018).
7. Gema, A.P., Winton, S., David, T., Derwin, S., Muhsin, S., Wikaria, G.: It Takes Two To Tango: Modification of Siamese Long Short Term Memory Network Attention Mechanism in Recognizing Argumentative Relations in Persuasive Essay. Comput. Intell. 449–459 (2017).
8. Xu, J., Yao, L., Li, L.: Argumentation based joint learning: A novel ensemble learning approach. PLoS One. 10, 1–21 (2015).
9. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. (2018).
10. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Daimlerchrysler, T.R., Shearer, C., Daimlerchrysler, R.W.: Step-by-step data mining guide. SPSS inc. 78, 1–78 (2000).
11. Webster, J., Watson, R.T.: Analyzing the past to prepare for the future : Writing a literature review Reproduced with permission of the copyright owner . Further reproduction prohibited without permission . MIS Q. (2002).
12. Vom Brocke, J., Simons, A., Riemer, K., Niehaves, B., Plattfaut, R., Cleven, A.: Standing on the shoulders of giants: Challenges and recommendations of literature search in information systems research. Commun. Assoc. Inf. Syst. (2015).
13. Toulmin, S.E.: Introduction to Reasoning. (1984).
14. Walton, D., Reed, C., Macagno, F.: Argumentation Schemes. Cambridge University Press, Cambridge (2008).
15. Oraby, S., Reed, L., Compton, R.: And That’s A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue. (2000).
16. 16. Hasan, K.S., Ng, V.: Why are You Taking this Stance ? Identifying and Classifying Reasons in Ideological Debates. 751–762 (2014).
17. Richard, A., Suhartono, D., Chowanda, A.D., Setiadi, C.J., Jessica, C.: Automatic Debate Text Summarization in Online Debate Forum. Procedia Comput. Sci. 116, 11–19 (2017).
18. Habernal, I., Gurevych, I.: Which argument is more convincing ? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM. 1589–1599 (2016).
19. Lippi, M., Torroni, P.: Argumentation Mining : State of the Art and Emerging Trends. 16, (2016).
20. Winkels, R., Douw, J., Veldhoen, S.: Experiments in automated support for argument reconstruction. ICAIL. (2013).
21. Sardianos, C., Katakis, I.M., Petasis, G., Karkaletsis, V.: Argument Extraction from News. 56–66 (2015).
22. Habernal, I., Gurevych, I.: Exploiting Debate Portals for Semi-Supervised Argumentation Mining in User-Generated Web Discourse. 2127–2137 (2015).
23. Schulz, C., Eger, S., Daxenberger, J., Kahse, T., Gurevych, I.: Multi-Task Learning for Argumentation Mining in Low-Resource Settings. (2017).
24. Rajendran, G., Chitturi, B., Poornachandran, P.: Stance-In-Depth Approach Deep Neural Approach to to Stance Stance Classification. Procedia Comput. Sci. 132, 1646–1653 (2018).
25. Daxenberger, J., Eger, S., Habernal, I., Stab, C., Gurevych, I.: What is the Essence of a Claim ? Cross-Domain Claim Identification. EMNLP. (2017).
26. Lippi, M., Torroni, P.: Argument mining: A machine learning perspective. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2015).
27. Palau, R.M., Moens, M.: Argumentation Mining : The Detection, Classification and Structure of Arguments in Text. ICAIL. (2009).
28. Ozyurt, I.B.: Automatic Identification and Classification of Noun Argument Structures in Biomedical Literature. 9, 1639–1648 (2012).
29. Zhang, G., Purao, S., Zhou, Y., Xu, H.: Argument detection in online discussion: A theory based approach. AMCIS Proc. 1–10 (2016).
30. Poudyal, P., Goncalves, T., Quaresma, P.: Experiments On Identification of Argumentative Sentences. 398–403 (2016).
31. Stab, C., Miller, T., Gurevych, I.: Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks. (2018).
32. Torrey, L., Shavlik, J.: Transfer Learning. Encycl. Sci. Learn. 3337–3337 (2012).
33. Jordan, M.I., Mitchell, T.M.: Machine learning: Trends, perspectives, and prospects, (2015).
34. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention Is All You Need. (2017).
35. Bahdanau, D., Cho, K., Bengio, Y.: Neural Machine Translation by Jointly Learning to Align and Translate. 1–15 (2014).
36. Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing, (2018).
37. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Distributed Representations of Words and Phrases and their Compositionality. CrossRef List. 1, 1–9 (2006).
38. Pennington, J., Socher, R., Manning, C.: Glove: Global Vectors for Word Representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014).
39. Habernal, I., Gurevych, I.: Argumentation Mining in user-generated web discourse. Comput. Linguist. (2017).
40. Stab, C., Gurevych, I.: Parsing Argumentation Structures in Persuasive Essays. (2017).
41. Varoquaux, G., Buitinck, L., Louppe, G., Grisel, O., Pedregosa, F., Mueller, A.: Scikitlearn. GetMobile Mob. Comput. Commun. (2017).
42. Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., Vanderplas, J., Joly, A., Holt, B., Varoquaux, G.: API design for machine learning software: experiences from the scikitlearn project. 1–15 (2013).
43. Chollet, F.: Keras Documentation, (2015).
44. Reed, C., Palau, R.M., Rowe, G., Moens, M.-F.: Language Resources for Studying Argument. In: Proceedings of the 6th conference on language resources and evaluation (2008).
45. Biran, O., Rambow, O.: Identifying justifications in written dialogs. In: Proceedings – 5th IEEE International Conference on Semantic Computing, ICSC 2011 (2011).
46. Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., Zettlemoyer, L.: Deep contextualized word representations. (2018).