We study the problem of performing automatic stance classification on social media with neural architectures such as BERT. Although these architectures deliver impressive results, their level is not yet comparable to the one of humans and they might produce errors that have a significant impact on the downstream task (e.g., fact-checking). To improve the performance, we present a new neural architecture where the input also includes automatically generated negated perspectives over a given claim. The model is jointly learned to make simultaneously multiple predictions, which can be used either to improve the classification of the original perspective or to filter out doubtful predictions. In the first case, we propose a weakly supervised method for combining the predictions into a final one. In the second case, we show that using the confidence scores to remove doubtful predictions allows our method to achieve human-like performance over the retained information, which is still a sizable part of the original input.

该论文提出了一种基于神经架构如BERT的自动立场分类方法，通过将自动生成的否定观点纳入输入，并设计弱监督方法将多个预测结果相结合来提高分类准确性，或者利用置信度分数过滤出可疑预测结果来达到类人表现的目的。

Tribrid: 立场分类及神经不一致检测