论文标题
通过情境化的话语处理理解政治
Understanding Politics via Contextualized Discourse Processing
论文作者
论文摘要
在对事件做出反应时，政客通常会有潜在的议程。各种事件的上下文中的论点反映了给定实体的一组一致的议程。尽管在审计语言模型（PLM）方面取得了最新进展，但这些文本表示并非旨在捕获这种细微的模式。在本文中，我们提出了一个由编码器和作曲家模块组成的构图读取器模型，该模型试图捕获和利用此类信息，以生成针对实体，问题和事件的更有效表示。这些表示形式通过推文，新闻发布，问题，新闻文章和参与实体的背景方式进行。我们的模型可以一次处理多个文档，并在几个问题或事件上为多个实体生成组成的表示。通过定性和定量的经验分析，我们表明这些表示有意义有效。
Politicians often have underlying agendas when reacting to events. Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity. In spite of recent advances in Pretrained Language Models (PLMs), those text representations are not designed to capture such nuanced patterns. In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that attempts to capture and leverage such information to generate more effective representations for entities, issues, and events. These representations are contextualized by tweets, press releases, issues, news articles, and participating entities. Our model can process several documents at once and generate composed representations for multiple entities over several issues or events. Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective.
