基于弱监督注意力的政治归属可视化评估工具
A Weakly-Supervised Attention-based Visualization Tool for Assessing Political Affiliation
摘要：
在这项工作中，我们试图微调一个弱监督的专家指导的深度神经网络（DNN），以确定政治派别。在这种情况下，立场检测用于确定政治派别或意识形态，其以低维空间中实体之间的相对接近度的形式构建。一个基于注意力的机制是用来提供模型的可解释性。使用静态和上下文嵌入的自然语言理解（NLU）深度神经网络被训练和评估。各种技术来可视化从网络生成的投影的可视化效率进行评估。这里给出了从数据摄取、处理和生成可视化的管道的概述。一个基于网络的框架，以促进这种互动和探索。总结了本研究的初步结果，并对今后的工作进行了展望。
英文摘要：
In this work, we seek to finetune a weakly-supervised expert-guided Deep Neural Network (DNN) for the purpose of determining political affiliations. In this context, stance detection is used for determining political affiliation or ideology which is framed in the form of relative proximities between entities in a low-dimensional space. An attention-based mechanism is used to provide model interpretability. A Deep Neural Network for Natural Language Understanding (NLU) using static and contextual embeddings is trained and evaluated. Various techniques to visualize the projections generated from the network are evaluated for visualization efficiency. An overview of the pipeline from data ingestion, processing and generation of visualization is given here. A web-based framework created to faciliate this interaction and exploration is presented here. Preliminary results of this study are summarized and future work is outlined.
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。
