英语新闻传播中的播音员意见检测
Detecting Anchors' Opinion in Hinghlish News Delivery
摘要：
人类喜欢表达自己的意见，渴望别人的意见。从各种来源挖掘和检测意见对个人、组织甚至政府都是有益的。新闻媒体就是这样的组织之一，在那里，一般的规范是不展示他们一方的观点。广告商是数字媒体的代言人，要求他们不要固执己见。然而，他们有时会偏离公认的规范，有意或无意地将自己的观点插入原本直截了当的新闻报道中。这主要是在辩论中看到的，因为它要求主播是自发的，从而使他们容易添加他们的意见。这种失误的后果可能导致有偏见的新闻，甚至在最坏的情况下支持某种议程。为此，我们提出了一个新的任务，主播的意见检测辩论。我们策划代码混合新闻辩论并开发ODIN数据集。数据集中共有2054个主播的话语被标记为固执己见或非固执己见。最后，我们提出了DetONADe，一个基于注意力的交互式框架，用于对主播的话语进行分类，并获得了0.703的最佳加权F1分数。全面的分析和评估显示了数据集和预测中许多有趣的模式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
