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Stance Prediction for Contemporary Issues: Data and Experiments

2020-05-29 19:54:07
Marjan Hosseinia, Eduard Dragut, Arjun Mukherjee
arXiv_CL
             
Abstract

We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by this http URL in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection.

Abstract (translated)

URL

https://arxiv.org/abs/2006.00052

PDF

https://arxiv.org/pdf/2006.00052.pdf

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