论文标题
现代问题的立场预测：数据和实验
Stance Prediction for Contemporary Issues: Data and Experiments
论文作者
论文摘要
我们调查了具有情感和情感信息的预训练的双向变压器是否在对当代问题的长期讨论中改善了立场检测。作为这项工作的一部分，我们创建了一个新颖的立场检测数据集，其中涵盖了419个不同有争议的问题及其相关的利弊，而procon.org则以非党派格式收集。实验结果表明，与较少参数少20倍的BERT相比，具有情感或情感信息的浅循环神经网络可以达到竞争结果。 We also use a simple approach that explains which input phrases contribute to stance detection.
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 procon.org 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.
