Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation
Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation
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DOI：
10.48550/arxiv.2211.01467
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Xinliang Frederick Zhang;Nick Beauchamp;Lu Wang
中科院分区：
文献类型：
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作者：
Xinliang Frederick Zhang;Nick Beauchamp;Lu Wang
姿态检测通常被构建为预测给定文本中对目标实体的情感。然而，这种设置忽略了源实体的重要性，即，谁在表达意见。在本文中，我们强调研究实体之间的相互作用时，推断立场的迫切需要。我们首先介绍了一个新的任务，实体到实体（E2E）的立场检测，其中总理模型，以确定其规范名称的实体和辨别立场联合。为了支持这项研究，我们策划了一个新的数据集，其中包含来自不同意识形态倾向的新闻文章的10，641个句子级别的注释。我们提出了一个新的生成框架，允许生成的规范名称的实体，以及他们之间的立场。我们进一步增强了模型与图形编码器，以总结实体活动和外部知识的实体。实验表明，我们的模型优于强大的比较大的利润率。进一步的分析表明，E2E立场检测理解媒体报价和立场景观，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 imperative 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 10,641 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.