Abstract
Accurate information from both sides of the contemporary issues is known to be an ‘antidote in confirmation bias’. While these types of information help the educators to improve their vital skills including critical thinking and open-mindedness, they are relatively rare and hard to find online. With the well-researched argumentative opinions (arguments) on controversial issues shared by Procon.org in a non-partisan format, detecting the stance of arguments is a crucial step to automate organizing such resources. We use a universal pretrained language model with weight-dropped LSTM neural network to leverage the context of an argument for stance detection on the proposed dataset. Experimental results show that the dataset is challenging, however, utilizing the pretrained language model fine-tuned on context information yields a general model that beats the competitive baselines. We also provide analysis to find the informative segments of an argument to our stance detection model and investigate the relationship between the sentiment of an argument with its stance.
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For more details visit https://www.procon.org/faqs.php.
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We use scikit-learn with default settings.
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We use their code shared on https://github.com/sheffieldnlp/stance-conditional.
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Acknowledgement
This work is supported in part by the U.S. NSF grants 1838145, 1527364, and 1838147. We also thank anonymous reviewers for their helpful feedback.
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Hosseinia, M., Dragut, E., Mukherjee, A. (2019). Pro/Con: Neural Detection of Stance in Argumentative Opinions. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2019. Lecture Notes in Computer Science(), vol 11549. Springer, Cham. https://doi.org/10.1007/978-3-030-21741-9_3
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DOI: https://doi.org/10.1007/978-3-030-21741-9_3
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