An educated and informed consumption of media content has become a challenge
in modern times. With the shift from traditional news outlets to social media
and similar venues, a major concern is that readers are becoming encapsulated
in "echo chambers" and may fall prey to fake news and disinformation, lacking
easy access to dissenting views. We suggest a novel task aiming to alleviate
some of these concerns -- that of detecting articles that most effectively
counter the arguments -- and not just the stance -- made in a given text. We
study this problem in the context of debate speeches. Given such a speech, we
aim to identify, from among a set of speeches on the same topic and with an
opposing stance, the ones that directly counter it. We provide a large dataset
of 3,685 such speeches (in English), annotated for this relation, which
hopefully would be of general interest to the NLP community. We explore several
algorithms addressing this task, and while some are successful, all fall short
of expert human performance, suggesting room for further research. All data
collected during this work is freely available for research.

本文研究如何检测能够有效反驳特定论点而非只是特定立场的文章，以解决社交媒体时代如何应对信息封锁和假新闻的问题。针对辩论演讲，给定一个演讲稿，我们的目标是从一组与其立场相反的演讲中找出直接反驳它的演讲。通过研究此问题，我们提供了一个包含 3,685 个数据样本（英文）的数据集，并探讨了几种算法。尽管某些算法表现成功，但都未达到人类专家的水平，这表明此方向存在进一步研究的空间。