论文标题
信息偏见检测的上下文
Context in Informational Bias Detection
论文作者
论文摘要
信息偏见是通过句子或条款传达的偏见，这些句子或条款提供了可以使读者对实体的意见的切线，投机或背景信息。从本质上讲，信息偏见与上下文有关，但是以前关于信息偏见检测的工作并未探讨句子以外的上下文的作用。在本文中，我们探讨了英语新闻文章中信息偏见的四种背景：邻近句子，全文，其他新闻发布者的同一事件文章以及来自同一领域的文章（但可能不同的事件）。我们发现，整合事件环境可以改善非常强大的基线分类性能。此外，我们在此任务上执行模型的第一个错误分析。我们发现，最表现的上下文模型在较长的句子和政治中间派文章中的句子上优于基准。
Informational bias is bias conveyed through sentences or clauses that provide tangential, speculative or background information that can sway readers' opinions towards entities. By nature, informational bias is context-dependent, but previous work on informational bias detection has not explored the role of context beyond the sentence. In this paper, we explore four kinds of context for informational bias in English news articles: neighboring sentences, the full article, articles on the same event from other news publishers, and articles from the same domain (but potentially different events). We find that integrating event context improves classification performance over a very strong baseline. In addition, we perform the first error analysis of models on this task. We find that the best-performing context-inclusive model outperforms the baseline on longer sentences, and sentences from politically centrist articles.
