论文标题
在Hinghlish新闻传递中检测锚的意见
Detecting Anchors' Opinion in Hinghlish News Delivery
论文作者
论文摘要
人类喜欢表达自己的意见，渴望他人的意见。采矿和检测各种来源的意见对个人，组织甚至政府都是有益的。一个这样的组织就是新闻媒体，其中一般规范不是要从他们的一边展示意见。锚是数字媒体的面孔，不得不自以为是。但是，有时，他们有目的或无意间将他们的观点与原本直接的新闻报道插入。这主要在辩论中可以看出，因为它要求锚是自发的，因此使他们很容易添加他们的意见。这种不幸的结果可能导致新闻有偏见，甚至可能在最糟糕的情况下支持某些议程。为此，我们提出了一项新颖的任务，以辩论中的锚点检测。我们策划混合新闻辩论并开发ODIN数据集。数据集中共有2054个锚点的话语被标记为自以为是或未开放的。最后，我们提出了Dastonade，这是一个基于交互式注意力的框架，用于分类锚的话语，并获得最佳的加权F1分数为0.703。彻底的分析和评估显示了数据集和预测中的许多有趣模式。
Humans like to express their opinions and crave the opinions of others. Mining and detecting opinions from various sources are beneficial to individuals, organisations, and even governments. One such organisation is news media, where a general norm is not to showcase opinions from their side. Anchors are the face of the digital media, and it is required for them not to be opinionated. However, at times, they diverge from the accepted norm and insert their opinions into otherwise straightforward news reports, either purposefully or unintentionally. This is primarily seen in debates as it requires the anchors to be spontaneous, thus making them vulnerable to add their opinions. The consequence of such mishappening might lead to biased news or even supporting a certain agenda at the worst. To this end, we propose a novel task of anchors' opinion detection in debates. We curate code-mixed news debates and develop the ODIN dataset. A total of 2054 anchors' utterances in the dataset are marked as opinionated or non-opinionated. Lastly, we propose DetONADe, an interactive attention-based framework for classifying anchors' utterances and obtain the best weighted-F1 score of 0.703. A thorough analysis and evaluation show many interesting patterns in the dataset and predictions.
