Abstract
Argument identification is the cornerstone of a complete argument mining pipeline. Furthermore, it is the essential key for a wide spectrum of applications such as decision making, assisted writing, and legal counselling. Nevertheless, most existing argument mining approaches are limited to a single, specific domain. The problem of building a robust system whose models are able to generalize over heterogeneous datasets remains fairly unexplored. In this paper, we tackle the argument identification task on two different datasets (Student Essays and Web Discourse), following two approaches: a classical machine learning approach and a DistilBert-based approach. Moreover, this paper sheds light on a new direction for researchers in this domain since we validate the principle of ensemble learning. In other words, we show that combining multiple approaches via a well stacked model improves the system performance. The results are very promising with respect to the recent findings in the literature.
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Notes
- 1.
Project source code is available at https://github.com/Alaa-Ah/Stacked-Model-for-Argument-Mining.
- 2.
We used Transformers from huggingface.co for our experiments.
- 3.
Here is an example (from Essays dataset) of an argument sentence that SVM fails to identify while DistilBERT succeeds: “Personally, I think both government and common people should have the responsibility for the environment, but we need to analyze some specific situations.”
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Acknowledgement
This work was supported by the French Ministry of Higher Education and Research. It has been also co-funded by the German Federal Ministry of Education and Research (BMBF) under the funding code 01|S20049.
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Alhamzeh, A., Bouhaouel, M., Egyed-Zsigmond, E., Mitrović, J., Brunie, L., Kosch, H. (2021). A Stacking Approach for Cross-Domain Argument Identification. In: Strauss, C., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2021. Lecture Notes in Computer Science(), vol 12923. Springer, Cham. https://doi.org/10.1007/978-3-030-86472-9_33
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