论文标题
具有知识图扩展的生成实体到实体的立场检测
Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation
论文作者
论文摘要
立场检测通常被构架，因为预测给定文本对目标实体的情感。但是，此设置忽略了来源实体的重要性，即表达意见的人。在本文中，我们强调了在推断立场时研究实体之间的相互作用的必要性。我们首先介绍了一项新任务，实体到实体（E2E）立场检测，素材模型以识别其规范名称中的实体并共同辨别立场。为了支持这项研究，我们策划了一个新的数据集，其中包括不同意识形态倾向的新闻文章中标记为句子级别的10,619个注释。我们提出了一个新颖的生成框架，以允许为实体及其中的立场产生规范的名称。我们通过图形编码器进一步增强了模型，以总结实体活动和围绕实体的外部知识。实验表明，我们的模型的表现优于大边缘的强烈比较。进一步的分析表明，E2E立场检测对于理解媒体报价和立场格局以及推断实体意识形态的有用性。
Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity (E2E) stance detection, which primes models to identify entities in their canonical names and discern stances jointly. To support this study, we curate a new dataset with 10,619 annotations labeled at the sentence-level from news articles of different ideological leanings. We present a novel generative framework to allow the generation of canonical names for entities as well as stances among them. We further enhance the model with a graph encoder to summarize entity activities and external knowledge surrounding the entities. Experiments show that our model outperforms strong comparisons by large margins. Further analyses demonstrate the usefulness of E2E stance detection for understanding media quotation and stance landscape, as well as inferring entity ideology.
