论文标题

在回声室外:检测反辩论演讲

Out of the Echo Chamber: Detecting Countering Debate Speeches

论文作者

Orbach, Matan, Bilu, Yonatan, Toledo, Assaf, Lahav, Dan, Jacovi, Michal, Aharonov, Ranit, Slonim, Noam

论文摘要

在现代,受过良好教育和知情的媒体内容消费已成为一个挑战。随着从传统新闻媒体转变为社交媒体和类似场所,一个主要问题是,读者正在被封装在“ Echo Chambers”中,并且可能是假新闻和虚假信息的猎物,因此无法轻松访问异议。我们建议一项新的任务,旨在减轻其中一些问题 - 检测最有效地应对论点的文章(而不仅仅是立场),而不仅仅是立场。我们在辩论演讲的背景下研究这个问题。鉴于这样的演讲,我们的目标是从关于同一主题的一系列演讲中识别出直接对抗它的演讲。我们提供了3,685个这样的演讲(用英语)提供的大型数据集(以英语为单位),并为此关系提供了注释,希望这对NLP社区充满信心。我们探索了解决此任务的几种算法,尽管有些算法取得了成功,但所有算法都没有专业的人类绩效,这暗示了进一步研究的空间。这项工作期间收集的所有数据均可自由地进行研究。

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.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源